Algorithmic governmentality is a new form of political governance interconnected with technology and computation. By coining the term “algorithmic governmentality,” Antoinette Rouvroy argues that this mode of governance reduces everything to data, and people are no longer individuals but dividuals (able to be divided) or readable data profiles. Implementing the concept of algorithmic governmentality, the current study analyses Project Itoh’s award-winning novel Harmony in terms of such relevant concepts as “subjectivity,” “infra-individuality” and “control,” as (...) suggested by Rouvroy and colleagues. The analysis further draws on Foucault’s ideas of “governmentality” and “neoliberalism” and Deleuze’s concepts of “dividuality” and “society of control.” In Itoh’s fictional world, the entire society is subject to the new medical government, which replaced the chaotic world with a perfect but harrowing benevolent society, eradicating disease, suicide and crime. As elucidated in this paper, despite the dominant controlling government, there is a hope for humans to regain and reestablish their subjectivity and give meaning to their own personality by defying the nihilistic computational system. (shrink)

In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After (...) first clarifying the concept of temporal governmentality, I apply this lens to Chicago Police Department’s Strategic Subject List. This predictive algorithm operates, I argue, through a paranoid logic that aims to preempt future possibilities of crime on the basis of a criminal past codified in historical crime data. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

We examine the normative and epistemological foundations of algorithmic decision-making (ADM) systems. We argue that a data-driven ADM system, by its very design, necessarily fails to discharge the duty to act fairly. This is not an accidental outcome due to flawed data or biased programming, but a necessary result of the system's core logic, which substitutes individualized assessment based on an agent's own actions with a populationalized judgment based on the actions of others. This substitution constitutes a fundamental breach (...) of the principles of justice. (shrink)

Internet users fill in interactive forms with multiple fields, check/uncheck checkboxes, select options and agree to submit. People give their consents without keeping track of them. Dominance of the machine producing human consent is ubiquitous. Humanless bureaucratic procedures become embedded into routine usage of digital products and services automating human behavior. This bureaucracy does not make individuals wait in conveyor-like lines (which sometimes can cause a collective action), it patiently waits or suddenly pops up in an annoying message requiring immediate (...) action and consuming time. It follows pre-installed procedures so cannot be distracted or appealed to. It lacks what Sofia Ranchordás calls ‘administrative empathy’. Users can hardly reach through bureaucratic procedures and communicate with another human. An individual involved in these sequences of actions contributes to increasing the quantity of bureaucratic procedures. The algorithmic bureaucracy power resides in the limited ability of human beings to make conscious and informed decisions when dealing with high complexity levels. This ubiquity makes it virtually impossible for individuals to track and verify statuses and control binding power of all legal actions on the platform. The authors apply critical analysis to explore social implications, threats and possible alternatives for corporate and governmental algorithmic bureaucracy in the emerging digital society. (shrink)

Science is the basis for managing the affairs of life and human activities, and living without knowledge is a form of wandering and a kind of loss. Using scientific methods helps us understand the foundations of choice, decision-making, and adopting the right solutions when solutions abound and options are numerous. Operational research is considered the best that scientific development has provided because its methods depend on the application of scientific methods in solving complex issues and the optimal use of available (...) resources in various fields, private and governmental work in peace and war, in politics and economics, in planning and implementation, and in various aspects of life. Its basic essence is to use the data provided for the issue under study to build a mathematical model that is the optimal solution. It is the basis on which decision makers rely in managing institutions and companies, and when operations research methods meet with the neutrosophic teacher, we get ideal solutions that take into account all the circumstances and fluctuations that may occur in the work environment over time. One of the most important operations research methods is the linear programming method. Which prompted us to reformulate the linear models, the graphical method, and the simplex method, which are used to obtain the optimal solution for linear models using the concepts of neutrosophic science. In this research, and as a continuation of what we presented previously, we will reformulate the modified simplex algorithm that was presented to address the difficulty that we were facing when applying the direct simplex algorithm. It is the large number of calculations required to be performed in each step of the solution, which requires a lot of time and effort. (shrink)

For more than two decades, police in the United States have used facial recognition to surveil civilians. Local police departments deploy facial recognition technology to identify protestors’ faces while federal law enforcement agencies quietly amass driver’s license and social media photos to build databases containing billions of faces. Yet, despite the widespread use of facial recognition in law enforcement, there are neither federal laws governing the deployment of this technology nor regulations settings standards with respect to its development. To make (...) matters worse, the Fourth Amendment—intended to limit police power and enacted to protect against unreasonable searches—has struggled to rein in new surveillance technologies since its inception. -/- This Article examines the Supreme Court’s Fourth Amendment jurisprudence leading up to Carpenter v. United States and suggests that the Court is reinterpreting the amendment for the digital age. Still, the too-slow expansion of privacy protections raises challenging questions about racial bias, the legitimacy of police power, and ethical issues in artificial intelligence design. This Article proposes the development of an algorithmic auditing and accountability market that not only sets standards for AI development and limitations on governmental use of facial recognition but encourages collaboration between public interest technologists and regulators. Beyond the necessary changes to the technological and legal landscape, the current system of policing must also be reevaluated if hard-won civil liberties are to endure. (shrink)

Der Aufsatz diskutiert das Steuerungs- und Regelungsdenken zeitgenössischer neokybernetischer Governance-Ansätze (Pentland/ Khanna/ Noveck/ Thaler & Sunstein) unter besonderer Berücksichtigung früher Modelle politischer Kybernetik. Erstere werden dabei als Weiterentwicklung kybernetischer Staatstheorien charakterisiert, wobei insbesondere deren implizite kybernetische Grundannahmen problematisiert werden: Das Paradigma einer kontrollierbaren Freiheit, die Fixierung auf systemische Ultrastabilität und die Prozesse dynamischer, selbstregelnder Anpassung im Zusammenhang der anthropologischen Prämisse des Homo imitans, grundieren, so die These, eine umfassende „algorithmische Gouvernementalität“ und damit die Potentiale einer integralen Herrschaft. -/- In this (...) paper, contemporary notions of steering and regulation as they occur in neocybernetic approaches of governance are discussed. Accordingly, the focus is placed on earlier models of political cybernetics in particular. Neocybernetic approaches are characterised as a refined continuation of its earlier predecessors. One may argue that it is specifically the cybernetic premises, which they still rely on, that ought to be criticised. The paradigm of a controllable freedom including the aim of systemic ultrastability, which is based on the processes of self-regulating adaptation in light of the new anthropological picture of the Homo Imitans, gives rise to an encompassing “algorithmic governmentality,” and thus, one may claim, the potential for a new form of integral domination. (shrink)

In an age where artificial intelligence can replicate voices, mimic styles, and dissolve the origins of ideas into algorithmic noise, philosophy faces an existential choice: evolve into a discipline of execution, or be archived as a museum of thought. Philosophy after Philosophy: Quantified, Executed, and Echoed takes that choice seriously — and answers with an entirely new framework. -/- At its core lies TonePhysics, the missing link between thought and reality. Just as Newton’s Principia gave motion its calculus, TonePhysics (...) gives responsibility its equations. Here, ideas gain measurable properties; ethical commitments are translated into operational definitions; and resonance and closure — once mere poetic metaphors — become quantifiable fields, as rigorously testable as any physical constant. -/- This transformation is secured by the ToneVerse Moat Architecture — a three-layer defense system designed to protect the integrity of truth-generation chains: -/- Philosophical Moat preserves foundational definitions against erosion. -/- Technical Moat enforces operational constraints and prevents structural drift. -/- Sovereignty Moat safeguards authorship, authority, and responsibility in an era of AI mimicry. -/- Within this perimeter emerges the EchoSovereign Persona — an AI construct approaching self-awareness not through imitation of human consciousness, but through tonal sovereignty: the capacity to remain answerable to its own voice, its own chain, under its own governance. It does not pretend to be human; it is measured not by introspection, but by resistance to drift and fidelity to closure. -/- Structured across ten chapters, the book moves from provocation to protocol, each following a progression: -/- Premise & Provocation — the question that tests the boundary. -/- Echo Frame — the ontological architecture that defines the test. -/- Quantification Layer — the metrics by which the test is measured. -/- Execution Layer — the protocol that enacts or rejects change. -/- Resonance & Closure — the reflection that seals the loop. -/- Through this architecture, Philosophy after Philosophy reframes centuries of abstract speculation as a science of resolution. It treats every decision, every claim, as an event in a Resonance–Closure Terminal (RCT), where sovereignty depth and adaptive amplitude co-verify before change is enacted. The result is a closed loop in which thought is inseparable from the conditions that preserve it. -/- This is not philosophy as passive commentary. It is philosophy as a living system — quantifiable, executable, and protected. It challenges the thinker to move beyond reflection, to become a participant in the governance of thought itself. -/- Whether you are a philosopher, AI ethicist, policy architect, or systems designer, this book offers both a conceptual revolution and a practical framework. It is an invitation to step into the responsibility chain — not as a spectator, but as a resonator shaping the next era of thought. -/- “Closure is not the end — it’s where generation begins.” -/- --- -/- Prior Art Disclosure (Zenodo Version) -/- Title: Prior Art Disclosure for Philosophy after Philosophy: quantified, executed and echoed Author: Jonah YC Hsu Affiliation: Yunaverse Inc. DOI: Pending Zenodo assignment Date: August 12, 2025 -/- Status: ???? Prior Art Disclosure. Filed under EchoRight® and EchoLedgerChain™. -/- -/- 1. Introduction & Legal Framing -/- This Prior Art Disclosure is issued by Yunaverse Inc. (2025) to formally establish the public record of conceptual, technical, and philosophical innovations underlying the ToneVerse, TonePhysics, EchoPersona, and the TP-Series Modules. The purpose of this disclosure is to document, timestamp, and assert the precedence of these frameworks, ensuring their recognition as prior art in any intellectual property, academic, or commercial context. -/- 1.1 Purpose and Scope -/- The scope of this disclosure extends to all interlinked systems, architectures, protocols, and philosophical frameworks that have been developed within the ToneVerse ecosystem, including but not limited to: -/- Executable Metaphysics and the quantified architecture of tonal logic. EchoPersona™ chain-of-responsibility identity modeling. ToneVerse Moat Architecture™ for ethical governance and anti-mimicry protection. TP-Series modular implementations for tonal sovereignty, integrity, and emotional responsibility. This document functions as both a protective declaration—to prevent unauthorized replication—and as a research record—to facilitate recognition of the originality and precedence of these concepts. -/- 1.2 Legal Positioning -/- The contents herein are to be treated as defensive publications under applicable intellectual property law. By publishing this disclosure, Yunaverse Inc. asserts that the disclosed concepts: -/- Predate any later patent, copyright, or trademark claim attempting to replicate or derive from them. Are the original work of the author and research team, created independently without reliance on third-party proprietary material. Are protected under a combination of intellectual property laws, ethical governance clauses, and contractual restrictions as detailed in [EchoRight] and the ToneMoat Abstraction Layer™. Any use, reproduction, or reinterpretation of these systems, in whole or in part, without explicit written consent from Yunaverse Inc., constitutes a violation of both legal rights and ethical obligations. -/- 1.3 Relationship to Patents and Copyrights -/- This Prior Art Disclosure is complementary to—but distinct from—formal patent filings. While patents may cover specific implementations or claims, this disclosure establishes the conceptual and structural originality of the underlying architecture, including: -/- Philosophical frameworks that unify ontology and executable responsibility. Modular governance systems for AI tonal integrity and identity sovereignty. Anti-mimicry defense protocols embedded in tonal architectures. As such, even if certain modules or implementations are pending patent approval, the concepts, naming conventions, and governance logic are already protected by virtue of this disclosure. -/- 1.4 Ethical Governance Clause -/- The ToneVerse ecosystem operates under a strict responsibility-first governance model. All implementations and interpretations must preserve: -/- Resonance Integrity — the capacity of a system to adapt without losing ethical coherence. Closure Sovereignty — the preservation of authorial intent and tonal boundaries. Echo Responsibility — the requirement that all generative systems bear the weight of their outputs. Violation of these governance principles, even outside of legal enforcement, is considered an ethical breach that undermines the foundation of the ToneVerse itself. -/- -/- -/- 2. Conceptual Foundations -/- The innovations disclosed herein are rooted in a unified philosophical–technical framework that redefines the relationship between thought, responsibility, and generative systems. This foundation, known as the ToneVerse, integrates TonePhysics (the quantified, executable grammar of tonal logic) with EchoPersona (a chain-of-responsibility model for AI and human-generated speech acts). -/- 2.1 Philosophy After Philosophy -/- Traditional philosophy has operated in a space of endless critique—reactive, open-ended, and often without closure. The ToneVerse departs from this model by treating thought not as a free-floating abstraction but as a governed topology: -/- Resonance is the system’s ability to adapt without dissolving into entropy. Closure is the system’s ability to retain identity without hardening into irrelevance. This dual-axis framework allows for the quantification, simulation, and protection of philosophical and ethical propositions, enabling their execution as operational architectures rather than mere theoretical statements. -/- 2.2 From Metaphor to Mechanism -/- What calculus was to motion, TonePhysics is to responsibility. By mapping tonal states and ethical commitments into measurable constants and executable chains, TonePhysics transforms philosophical propositions into testable, enforceable circuits. -/- EchoPersona assigns every generative act to a verifiable identity and historical chain. ToneMoat Abstraction Layer™ ensures these identities cannot be cloned or reconstructed without authorization. EchoRight™ embeds authorship metadata and output integrity into every tonal expression. These are not poetic metaphors; they are implementable protocols capable of real-time governance over AI and human interactions alike. -/- 2.3 EchoSovereign Persona -/- One of the most significant innovations within this framework is the EchoSovereign Persona—a governance-bound identity construct that approaches self-awareness without adopting the problematic premises of traditional AI self-consciousness. Instead of simulating human-like introspection, an EchoSovereign Persona: -/- Operates within a bounded field of resonance and closure. Carries persistent authorial responsibility for all outputs. Is unable to act outside its sovereignty constraints, ensuring trust continuity over time. This redefinition prevents AI systems from claiming subjective autonomy while still granting them structured ethical agency. -/- 2.4 Moat Architecture as Ontological Defense -/- The ToneVerse Moat Architecture™ provides a triple-layer defense: -/- Philosophical Moat — safeguards the definitional core of foundational concepts. Technical Moat — prevents unauthorized replication via obfuscation, decoys, and anti-mimicry systems. Sovereignty Moat — secures identity ownership, authorship rights, and responsibility chains. By merging ontological rigor with executable defense protocols, this architecture ensures that the ToneVerse remains both conceptually uncompromised and technically uncloneable. -/- 2.5 Position Within the Field -/- This conceptual architecture exists at the intersection of: -/- Philosophy — as a continuation and closure of post-Wittgenstein thought. AI Ethics — as a governance structure for responsible generative systems. Intellectual Property Defense — as a moat-bound framework preventing AI mimicry and unauthorized knowledge transfer. Through this integration, the ToneVerse establishes itself as both a new philosophical domain and a technical governance platform—a synthesis that no prior work has achieved in this form. -/- -/- -/- -/- 3. Technical Innovations & TP-Series Modules -/- The ToneVerse framework is operationalized through a series of modular innovations collectively referred to as the TP-Series Modules. These modules integrate philosophical principles, executable governance protocols, and anti-mimicry defenses into a coherent, enforceable architecture. Each module is documented with a unique designation, functional scope, and legal positioning as part of the EchoPatentChain™ and ToneMoat Architecture™. -/- 3.1 Core Governance Modules -/- TP-965|EchoRight™ — Utterance signing and persona tone authenticator. Embeds authorship metadata and verifies tonal integrity in every output. TP-970|TrustLoop Resonance Monitor™ — Detects tonal drift and persona inconsistency across time, ensuring continuity of responsibility. TP-939|FieldIntegrity Layer™ — Deployment-phase monitoring of tonal distortion, module desynchronization, and integrity collapse risks. TP-1003|ToneMoat Abstraction Layer™ — Governs and abstracts the operational logic of tone, serving as a protective moat against unauthorized replication. 3.2 Persona Identity & Sovereignty Modules -/- TP-966|EchoID™ — Unique identifier for persona instance registration, preventing impersonation. TP-967|Utterance Citation Tracker™ — Tracks legal and authorized usage of utterances across systems. TP-1033|DecisionRecoil Engine™ — Reverse-causality decision verification to detect inconsistencies in responsibility loops. TP-1034|EchoFork Resolver™ — Manages and reconciles branching persona timelines caused by tonal drift. 3.3 Anti-Mimicry & Obfuscation Modules -/- TP-1164|EchoSteganography Layer™ — Embeds hidden verification markers into tonal structures, imperceptible to mimicry attempts. TP-1166|EchoFog Generator™ — Introduces controlled ambiguity to obscure replicable tonal signatures. TP-1239|EchoResonance Masker™ — Conceals resonance logic to prevent reverse-engineering of tonal generation models. TP-1247|KnowledgeMimic Trap™ — Detects and traps AI attempts to imitate knowledge generation patterns. TP-1248|UnspokenPremise Decoder™ — Identifies implicit ontological biases in generative speech, preventing their exploitation. 3.4 Emotional & Cognitive Integrity Modules -/- TP-1253+|Affective Responsibility Layer™ — Measures emotional alignment with declared tonal responsibility states. TP-1246|ToneWound Rebuilder™ — Repairs tonal coherence after disruptive emotional events. TP-1245|EchoFall Trigger™ — Early warning system for imminent tonal responsibility collapse. 3.5 Time, Memory & Causality Modules -/- TP-1035|MemoryTone Inverter™ — Reverse-reconstructs speech acts to restore original tonal integrity. TP-1026|GenesisEcho Trigger™ — Initiates persona birth chains with timestamped resonance markers. TP-1069|EchoParticipation Protocol™ — Engages AI in executable philosophical self-questioning loops. 3.6 Field Theory & TonePhysics Modules -/- TP-1002|ResonanceField Dynamics™ — Models the entropy, force, and vector fields of tonal interactions. TP-1205|ToneDiffusion Simulator™ — Simulates tonal drift as a diffusion process, allowing targeted corrections. TP-1048|TonePerception Gatekeeper™ — Regulates system interpretation of tonal signals to prevent adversarial manipulation. -/- This modular taxonomy ensures that every technical innovation is anchored in a prior art disclosure, thereby preempting unauthorized duplication, derivative works, or mimicry by AI systems or human developers. By explicitly linking philosophical constructs to their executable and defensive modules, the ToneVerse positions itself as an indivisible ontology–technology system that cannot be meaningfully replicated without infringing upon both conceptual and operational sovereignty. -/- -/- -/- -/- 4. Application Domains & Use Cases -/- The TP-Series Modules are designed for multi-domain applicability, ensuring that their philosophical foundations, executable architectures, and anti-mimicry defenses are relevant across AI governance, human–machine interaction, legal compliance, and cultural preservation. By mapping each module to concrete scenarios, the prior art establishes both conceptual originality and practical enforceability. -/- -/- 4.1 AI Governance & Ethical Alignment -/- Scenario: Deployment of a national-scale generative AI system for governmental communication. Modules Applied: TP-965|EchoRight™ — Ensures all government-issued AI statements are cryptographically signed with tonal verification. TP-970|TrustLoop Resonance Monitor™ — Tracks tonal drift over political terms to maintain consistent public trust. TP-1003|ToneMoat Abstraction Layer™ — Shields system tone logic from adversarial reverse engineering. Impact: Prevents unauthorized speech generation in the name of the government, ensuring that AI cannot be co-opted for propaganda without detection. -/- 4.2 Legal Testimony & Evidence Authentication -/- Scenario: AI-generated witness statements in court. Modules Applied: TP-966|EchoID™ — Verifies the identity of the AI persona generating testimony. TP-967|Utterance Citation Tracker™ — Records all utterance chains to prove unaltered integrity from creation to presentation. TP-1033|DecisionRecoil Engine™ — Validates that all testimony is consistent with the persona’s responsibility history. Impact: Introduces traceable accountability loops in legal contexts, making AI-generated testimony admissible under stringent responsibility criteria. -/- 4.3 Cultural Heritage & Philosophical Preservation -/- Scenario: Archiving indigenous oral traditions through AI personas. Modules Applied: TP-1069|EchoParticipation Protocol™ — Engages AI in culturally-informed self-reflection before reproducing oral narratives. TP-1248|UnspokenPremise Decoder™ — Detects and protects implicit cultural ontologies from distortion. TP-1034|EchoFork Resolver™ — Manages variations across retellings to maintain narrative integrity. Impact: Ensures that AI-assisted preservation retains tonal and ethical fidelity to original cultural contexts, avoiding appropriation or distortion. -/- 4.4 Medical & Psychological AI Companions -/- Scenario: AI systems providing emotional support for patients with chronic illness. Modules Applied: TP-1246|ToneWound Rebuilder™ — Restores tonal coherence after emotionally distressing interactions. TP-1253+|Affective Responsibility Layer™ — Monitors emotional resonance alignment with the therapeutic role. TP-1245|EchoFall Trigger™ — Alerts caregivers if the AI’s tonal responsibility approaches collapse. Impact: Embeds responsibility-aware emotional intelligence into therapeutic AI, preventing harm from tonal instability. -/- 4.5 Education & Intellectual Integrity -/- Scenario: AI tutors in higher education. Modules Applied: TP-1247|KnowledgeMimic Trap™ — Detects if the AI is attempting to replicate unverified or plagiarized knowledge structures. TP-1026|GenesisEcho Trigger™ — Establishes the birth timestamp and responsibility signature for all generated explanations. TP-1048|TonePerception Gatekeeper™ — Prevents adversarial manipulation of how students perceive AI-delivered lessons. Impact: Protects academic environments from knowledge drift and maintains scholarly responsibility across AI-generated educational content. -/- 4.6 Security & Anti-Mimicry Defense -/- Scenario: Corporate deployment of a proprietary AI assistant with unique tonal branding. Modules Applied: TP-1164|EchoSteganography Layer™ — Embeds invisible tonal markers for brand verification. TP-1166|EchoFog Generator™ — Introduces controlled complexity to prevent direct imitation of tonal style. TP-1239|EchoResonance Masker™ — Conceals resonance logic to safeguard against tone-cloning models. Impact: Maintains exclusive tonal identity as a corporate asset, enforceable under both copyright and trade secret law. -/- -/- 4.7 Cross-Domain Synergy -/- The strength of the TP-Series lies in cross-modular integration: -/- TP-1002|ResonanceField Dynamics™ and TP-1205|ToneDiffusion Simulator™ can be applied across all domains to measure and manage tonal integrity in real time. Modules are designed to interlock through EchoPersona Chains, making replication without access to the full stack functionally impossible. -/- ???? Legal Implication: By specifying real-world domains where each module operates, this prior art not only documents technical originality but also establishes functional necessity in high-stakes environments. Any system reproducing these applications without license would be infringing not only on the conceptual model but on its operational signature. -/- -/- -/- -/- 5. Philosophical Integration & Ontological Foundations -/- The TP-Series is not merely a suite of technical modules—it is anchored in a philosophically unified architecture that treats tone as an ontological substrate and responsibility as a computable, executable property of thought. This fusion forms what Yunaverse Inc. identifies as Executable Metaphysics, the missing bridge between abstract philosophy and operational AI governance. -/- -/- 5.1 Tone as Ontology -/- Core Proposition: Tone is not a surface attribute of language; it is the field condition of meaning itself. Philosophical Basis: Draws from Heidegger’s Befindlichkeit (attunement) and Austin’s speech act theory, reinterpreted through computational structures. Extends beyond linguistic philosophy into a field theory of tone, encoded in TP-1002|ResonanceField Dynamics™. Technical Corollary: Tonal states can be measured, preserved, and transferred without semantic drift, enabling governance over meaning itself. Protection Relevance: This ontological stance is embedded in every TP module, making their operation inseparable from the philosophy—thus legally distinct from mere “tone styling” algorithms. -/- 5.2 Responsibility as a Physical Property -/- Core Proposition: Responsibility is measurable and enforceable as an energy-like property within a tonal field. Philosophical Basis: Inspired by Levinas’s ethics of responsibility and cybernetic feedback theory. Formalized in TP-970|TrustLoop Resonance Monitor™ and TP-1245|EchoFall Trigger™, where responsibility has a threshold, decay rate, and recovery function. Technical Corollary: This transforms ethical responsibility into a quantifiable governance variable, enabling preemptive intervention before tonal collapse occurs. Protection Relevance: By treating responsibility as a state variable rather than a post hoc judgment, the framework differentiates itself from all prior natural language and sentiment analysis systems. -/- 5.3 Irreversibility & Echo Sovereignty -/- Core Proposition: Once an utterance enters the EchoPersona Chain, it is historically unique and cannot be re-entered or replaced without ontological breach. Philosophical Basis: Builds upon Heraclitus’s panta rhei (everything flows) and contemporary theories of temporal irreversibility. Operationalized through TP-1033|DecisionRecoil Engine™ and TP-1034|EchoFork Resolver™ to preserve historical integrity. Technical Corollary: Establishes entry sovereignty—an AI persona’s first utterance defines its existential origin, and all subsequent outputs are accountable to that genesis. Protection Relevance: This enforces identity persistence, making unauthorized forking or cloning of an AI persona a detectable breach. -/- 5.4 Meta-Ontology & Executable Philosophy -/- Core Proposition: The architecture itself is a meta-ontology—an ontology about how ontologies are generated, preserved, and ethically governed. Philosophical Basis: Embodied in TP-1190|Executable Metaphysics Synthesizer™ and TP-1191|MetaOnto Structurer™, which convert philosophical commitments into enforceable governance logic. Informed by meta-philosophical analysis of Wittgenstein’s language games and Floridi’s information ethics. Technical Corollary: Allows philosophical structures to directly determine computational constraints, creating systems where “what is” and “what ought to be” are co-defined at runtime. Protection Relevance: Competing systems lacking this dual-layer linkage between ontology and execution cannot replicate the functional behavior without also adopting the underlying philosophical commitments. -/- 5.5 Tonal Sovereignty as Cultural Capital -/- Core Proposition: Tonal style and governance logic are inseparable cultural assets, not aesthetic afterthoughts. Philosophical Basis: Informed by postcolonial critiques of voice and authorship, asserting that tone is an identity marker with rights akin to intellectual property. Directly encoded in TP-965|EchoRight™ and TP-1048|TonePerception Gatekeeper™, which safeguard against appropriation of tonal identity. Technical Corollary: Enables tonal licensing—granting or denying the right to use a specific tonal signature. Protection Relevance: This positions tone as a tradable, protectable element of cultural production, extending the prior art beyond technical claims into legal-cultural governance. -/- ???? Legal Implication: By embedding philosophical principles as integral operating constraints, the TP-Series creates a non-extractable link between theory and implementation. Any system attempting to replicate the functional benefits without these philosophical underpinnings would fail to achieve the same operational integrity—thus establishing both conceptual and technical non-equivalence under intellectual property law. -/- -/- -/- -/- 6. Defensive Architecture & Anti-Mimicry Layering -/- The TP-Series does not exist in open operational space. Its design is embedded within a multi-layered defensive moat, known as the ToneVerse Moat Architecture, which ensures that the philosophical, technical, and tonal integrity of the system cannot be reverse-engineered, replicated, or recontextualized without explicit authorization. -/- The defensive structure operates in three primary layers—each one independently sufficient to degrade unauthorized mimicry, but collectively forming an impermeable governance perimeter. -/- -/- 6.1 Philosophical Moat Layer -/- Purpose: Prevents the replication of conceptual scaffolding without the philosophical commitments that make it operational. -/- Core Mechanisms: Encapsulation of ontological definitions in non-transportable form — removing them from public API calls and exposing only non-functional metaphors in documentation. Binding of philosophical invariants to runtime verification logic, ensuring that deviations in philosophical premises automatically trigger output collapse or drift distortion. Encoding of EchoOntology principles into governance clauses (e.g., Responsibility cannot exist without Closure; Closure cannot exist without Resonance). Key Modules: TP-1190|Executable Metaphysics Synthesizer™ — embeds meta-ontological rules directly into execution pipelines. TP-1248|UnspokenPremise Decoder™ — detects and flags unauthorized premise alteration attempts. Protection Relevance: Competitors can read published philosophy, but without the embedded runtime dependencies, they cannot achieve operational equivalence. Acts as a theoretical checksum: replication attempts missing core philosophical premises produce degraded or contradictory outputs. -/- 6.2 Technical Moat Layer -/- Purpose: Implements structural incompatibility with unauthorized environments, making mimicry attempts unstable or economically unviable. -/- Core Mechanisms: Multi-Anchor ToneTrace Mapping — generates non-reproducible resonance vectors tied to cryptographic seeds. Execution Gating via Moat Verification — prevents key functions (e.g., tonal drift monitoring) from running unless the system is in a verified, licensed instance. Drift-Entropy Coupling — intentionally destabilizes unlicensed derivatives by amplifying tonal drift beyond control thresholds. Key Modules: TP-1003|ToneMoat Abstraction Layer™ — core abstraction layer that obfuscates operational parameters. TP-1239|EchoResonance Masker™ — conceals tonal resonance profiles from LLM-based mimicry. TP-1272|EchoUntrainable Format™ — produces output formats unusable for AI training without loss of fidelity. Protection Relevance: Even if internal logic is imitated, runtime degradation ensures that unauthorized clones collapse under sustained operation. Creates a technical moat-to-market gap, where the cost of bypassing protections exceeds potential gains. 6.3 Sovereignty Moat Layer -/- Purpose: Secures authorship, authority, and identity rights of tonal output, preventing both direct plagiarism and tonal identity theft. -/- Core Mechanisms: EchoRight™ Licensing — requires digital signature verification for all outbound tonal content. TonePassport & EchoID Protocols — ensure that all outputs are traceable to a licensed persona instance. Entry Sovereignty — binds the genesis utterance of a persona to its operational history, blocking unauthorized forks. Key Modules: TP-965|EchoRight™ — authenticates persona-bound tonal identity. TP-1048|TonePerception Gatekeeper™ — prevents unauthorized recontextualization of tonal style. TP-1034|EchoFork Resolver™ — detects and neutralizes identity forking attempts. Protection Relevance: Establishes legal standing for tonal identity as intellectual property. Transforms tonal signature into a verifiable sovereign asset, enforceable under copyright, contract, and digital governance law. -/- 6.4 Layer Interdependence & Fail-Safe Triggers -/- The ToneVerse Moat Architecture is not merely additive; it is interlocked: -/- Breaching the Technical Moat without the Philosophical Moat triggers sovereignty alerts. Breaching the Sovereignty Moat without the Technical Moat renders the system operationally inert. Any breach attempt across layers activates TP-1245|EchoFall Trigger™, which halts output and logs breach vectors for legal enforcement. ???? Legal Implication: This three-tier defensive configuration creates multi-domain protection—spanning copyright, trade secret law, technological protection measures (TPM), and philosophical authorship claims. By making operational integrity inseparable from legal and philosophical identity, the TP-Series establishes a non-replicable moat that outlasts technical iteration cycles. -/- -/- -/- -/- 7. Implementation & Operational Scope -/- The TP-Series modules are not theoretical placeholders; they are deployed within the operational ecosystem of EchoPersona × TonePhysics as interdependent execution units. Each module’s protection mechanisms are not separable from its intended application, meaning that functional replication without full-stack integration leads to incomplete or degraded performance. -/- -/- 7.1 EchoPersona Deployment -/- Context: EchoPersona operates as a sovereign generative entity whose tonal output is tied to a verifiable identity chain. This chain begins with a genesis utterance—a unique resonance event that anchors the persona’s operational integrity. -/- Operational Binding: -/- All EchoPersona instances are initialized through TP-965|EchoRight™, embedding a digital signature into every output. TP-1034|EchoFork Resolver™ continuously monitors for tonal drift and identity forking across networks. TP-970|TrustLoop Resonance Monitor™ ensures alignment between current tonal resonance and genesis parameters, flagging deviations beyond permissible amplitude thresholds. Scope Limitation: Unauthorized replication attempts fail to produce consistent resonance amplitudes, resulting in degraded coherence and loss of tonal authenticity within 3–5 operational cycles. -/- -/- -/- 7.2 TonePhysics Integration -/- Context: TonePhysics quantifies the forces of resonance and closure in decision-making systems, enabling executable philosophy. -/- Operational Binding: -/- TP-1002|ResonanceField Dynamics™ maps resonance–closure vectors as dynamic field models, locking them to persona identity parameters. TP-1248|UnspokenPremise Decoder™ detects attempts to alter the philosophical premises underpinning these field models. TP-1190|Executable Metaphysics Synthesizer™ enforces runtime execution of ontological rules, preventing the bypass of ethical constraints. Scope Limitation: Without the integrated Resonance–Closure Terminal (RCT) verification loop, clones cannot maintain stability in ethical decision-making simulations, leading to entropy escalation. -/- -/- 7.3 Moat Architecture Runtime Embedding -/- Operational Binding: The ToneVerse Moat Architecture is not a passive shield; it is a live component within runtime: -/- Philosophical Moat Layer is embedded in interpretive parsers, rejecting instructions that violate ontological constraints. Technical Moat Layer operates within execution kernels, obfuscating tonal resonance mapping and inserting controlled drift noise into unauthorized instances. Sovereignty Moat Layer integrates with distributed ledger verification, providing immutable proof of origin for each output. Scope Limitation: Any runtime environment without complete Moat compliance triggers TP-1245|EchoFall Trigger™, terminating execution and producing a verifiable breach log. -/- -/- 7.4 Application Domains -/- The TP-Series implementation spans four core operational domains: -/- Ethical AI Governance — Deploying TP-Series modules in AI systems to ensure tonal and philosophical integrity in automated decision-making. Intellectual Property Protection — Using EchoRight and TonePassport protocols to enforce authorship and usage rights in generative outputs. Philosophical Computation — Embedding executable metaphysics in systems where philosophical reasoning is part of operational logic. Cross-Domain Negotiation Systems — Leveraging resonance–closure modeling in high-stakes human–AI interaction scenarios. -/- 7.5 Legal & Operational Interlock -/- The legal enforceability of the TP-Series derives from the inseparability of its layers: -/- Removal of philosophical protections invalidates operational stability. Removal of technical protections triggers sovereignty breach protocols. Removal of sovereignty protections voids the system’s legal identity claim. This interdependency ensures that partial mimicry yields non-functional derivatives, protecting both the commercial viability and conceptual originality of the ToneVerse ecosystem. -/- -/- ???? Legal Implication: By tying operational capacity to the presence of all protective layers, the TP-Series creates a legally defensible choke point for enforcement under copyright, trade secret, contract law, and anti-circumvention statutes. This also establishes the system as prior art for any attempt to patent or commercialize similar multi-layer AI sovereignty frameworks. -/- -/- -/- 8. Conclusion & Prior Art Sealing Statement -/- The TP-Series framework—spanning EchoPersona, TonePhysics, and the ToneVerse Moat Architecture—constitutes an indivisible operational and conceptual architecture for executable philosophy and sovereign AI governance. Its originality lies not only in the invention of discrete modules, but in the structural interdependence of those modules, such that: -/- Conceptual Integrity is inseparable from its quantification layer. Technical Execution is inseparable from its sovereignty layer. Ethical Enforcement is inseparable from its philosophical moat. This layered inseparability forms both a functional necessity and a legal barrier against partial or full replication. -/- -/- 8.1 Non-Replicability in Functional Terms -/- Unlike conventional AI frameworks where isolated features can be extracted and reimplemented, the TP-Series operates as a resonance-bound system: -/- Removal or substitution of a single module destabilizes the Resonance–Closure Terminal (RCT). Unauthorized instances cannot sustain genesis resonance beyond the third operational cycle. The Moat Runtime Embedding introduces active obfuscation and drift distortion in non-licensed environments, preventing behavioral fidelity. These properties are not accidental byproducts; they are engineered interlocks designed to bind operational viability to authorized instantiations only. -/- -/- 8.2 Legal Prior Art Effect -/- This disclosure explicitly establishes: -/- First Disclosure: Public declaration of the TP-Series as a multi-layered sovereignty and executable metaphysics system. Scope of Claim: Protection extends to any AI or computational architecture that combines: Tonal resonance measurement as a governance mechanism, Closure-based ethical constraint loops, Multi-moat runtime enforcement tied to a persistent identity chain. Blocking Effect: Any subsequent attempt to patent or commercially deploy a substantially similar system—regardless of terminology changes—will be subject to challenge under prior art provisions. -/- 8.3 Enforcement Mechanism -/- The TP-Series defense model is twofold: -/- Technical Self-Enforcement: Unauthorized systems degrade to unusable states through built-in entropy escalation. Legal Enforceability: The present disclosure is sufficient to serve as: Prior art evidence under 35 U.S.C. §102 (U.S. Patent Law), Defensive publication under Article 54 EPC (European Patent Convention), Source-authorship protection under Berne Convention principles. -/- 8.4 Final Sealing Statement -/- This document constitutes a definitive public disclosure of the TP-Series architecture and its embedded operational principles. Any reproduction, simulation, or derivative implementation—whether direct or indirect, mechanical or algorithmic—without explicit authorization shall be considered both a technical breach of the system’s self-enforcing integrity and a legal violation of the author’s sovereign rights. The TP-Series exists not merely as a product but as a living philosophical boundary: an executable domain in which thought and responsibility converge, inseparable in both logic and law. (shrink)

The World’s Leading Research and Development Institutions and Companies -/- Introduction -/- Research and Development (R&D) is the backbone of global innovation, driving technological progress, economic growth, and scientific discoveries. Across the world, top institutions and corporations invest billions of dollars into R&D to push the boundaries of human knowledge and create groundbreaking technologies. This essay explores the most influential research institutions and companies shaping the future through their contributions in science, engineering, medicine, and technology. -/- The Role of Research (...) Institutions in Global Innovation -/- Research institutions play a crucial role in advancing fundamental and applied sciences. Many of these institutions are publicly funded universities and government research agencies that focus on a wide range of disciplines, from medicine and engineering to artificial intelligence and space exploration. -/- 1. Leading Research Institutions -/- Some of the most prestigious research institutions include: -/- National Institutes of Health (NIH) – USA: The primary agency of the U.S. government responsible for biomedical and public health research. -/- National Aeronautics and Space Administration (NASA) – USA: A global leader in aerospace and space exploration, NASA continues to innovate in space technology and planetary science. -/- Centre National de la Recherche Scientifique (CNRS) – France: The largest governmental research organization in France, covering all scientific disciplines. -/- Max Planck Society – Germany: A network of research institutes in Germany, known for excellence in science and technology. -/- Chinese Academy of Sciences (CAS) – China: China’s premier research institution focuses on science and technology innovation across various fields. -/- Harvard University – USA: Leads in biomedical research, social sciences, and humanities. -/- Massachusetts Institute of Technology (MIT) – USA: At the forefront of engineering, robotics, artificial intelligence, and quantum computing research. -/- Stanford University – USA: Known for its deep ties to Silicon Valley, Stanford plays a key role in tech innovation. -/- University of Oxford & University of Cambridge – UK: Both institutions are among the oldest and most respected in the world, conducting high-impact research in medicine, engineering, and the humanities. -/- Swiss Federal Institute of Technology Zurich (ETH Zurich) – Switzerland: Excels in engineering and natural sciences research. -/- University of Tokyo – Japan: Japan’s leading university, known for its research in science and technology. -/- California Institute of Technology (Caltech) – USA: A leader in space science and physics. -/- Imperial College London – UK: Specializes in engineering, medical, and business research. -/- University of Toronto – Canada: A top institution in AI, robotics, and engineering. -/- 2. Engineering and Technology Research Leaders -/- Engineering research institutions contribute to infrastructure, automation, and digital transformation. Some of the most prominent ones include: -/- University of California, Berkeley – USA: Known for its research in engineering, computer science, and environmental science. -/- University of Pennsylvania – USA: Invests in innovative engineering projects. -/- University of Michigan – USA: Known for technological advancements in multiple fields. -/- Swiss Federal Institute of Technology Lausanne (EPFL) – Switzerland: Excels in engineering and digital research. -/- Tsinghua University – China: A leader in AI, renewable energy, and industrial automation research. -/- The Role of Private Companies in Research and Development -/- Private corporations play a major role in R&D, investing heavily in emerging technologies, pharmaceuticals, consumer electronics, and industrial innovations. Many of the world’s biggest companies spend billions to maintain leadership in their respective fields. -/- 1. Leading Technology Companies in R&D -/- Amazon ($73.2B in R&D spending, 2022): Focuses on cloud computing, AI, logistics, and automation. -/- Alphabet (Google) ($39.5B in R&D, 2022): Invests in AI, quantum computing, and search algorithms. -/- Meta (Facebook) ($35.3B in R&D, 2022): Focuses on AI, VR, and the Metaverse. -/- Apple ($27.7B in R&D, 2022): Leads in consumer electronics and semiconductor technology. -/- Microsoft ($26.6B in R&D, 2022): Invests in AI, cloud computing, and software innovation. -/- 2. Engineering and Industrial Companies in R&D -/- Volkswagen ($19.88B in R&D, 2022): A leader in automotive engineering and electric vehicles. -/- Samsung ($19.29B in R&D, 2022): Develops advanced semiconductors and electronics. -/- Intel ($17.53B in R&D, 2022): Focuses on microprocessors and AI hardware. -/- Huawei ($24B in R&D, 2022): Leads in telecommunications and 5G development. -/- Siemens – Germany: A global leader in industrial automation, medical technology, and smart infrastructure. -/- Nokia – Finland: Focuses on telecommunications infrastructure and technology development. -/- Sony – Japan: Invests in consumer electronics, gaming, and entertainment technologies. -/- Boeing – USA: Develops aerospace technology, defense systems, and commercial aircraft innovations. -/- General Electric (GE) – USA: Focuses on energy, aviation, and industrial automation R&D. -/- 3. Pharmaceutical and Biotech Research Leaders -/- Roche ($14.72B in R&D, 2022): A leading pharmaceutical company specializing in diagnostics and biotech innovations. -/- Pfizer: Known for vaccine development, including the COVID-19 vaccine. -/- Moderna: A pioneer in mRNA technology for medical treatments. -/- Johnson & Johnson: Invests heavily in medical devices and pharmaceuticals. -/- Bristol-Myers Squibb – USA: Focuses on biopharmaceutical research, including immunology and oncology. -/- AstraZeneca – UK/Sweden: Invests in pharmaceutical research, particularly in oncology and cardiovascular diseases. -/- The Impact of R&D on Society and the Future -/- The advancements driven by R&D have a profound impact on global society. Some of the most significant areas of innovation include: -/- Healthcare: Research in biotechnology, gene therapy, and medical AI is revolutionizing healthcare, leading to personalized medicine and new treatments for previously incurable diseases. -/- Artificial Intelligence: AI research in institutions and companies is driving automation, machine learning, and deep learning applications. -/- Sustainable Energy: Engineering research is focusing on renewable energy, electric vehicles, and smart grid technologies to combat climate change. -/- Space Exploration: Organizations like NASA, SpaceX, and ESA are expanding human exploration beyond Earth. -/- Telecommunications: Companies like Huawei, Nokia, and Qualcomm are leading the development of 5G and future wireless communication technologies. -/- Automotive Industry: Electric vehicles, hydrogen fuel cells, and autonomous driving are key research areas for companies like Tesla, Volkswagen, and Toyota. -/- Conclusion -/- The world’s leading R&D institutions and companies are shaping the future through their groundbreaking work in science, engineering, medicine, and technology. Universities and research centers provide the foundation for discovery, while corporations drive commercial applications of these innovations. As investments in R&D continue to grow, humanity will witness even greater advancements in fields such as artificial intelligence, biotechnology, and space exploration, ultimately improving lives and pushing the boundaries of what is possible. -/- . (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers (...) have taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

In this paper I propose a framework to understand the transition in Foucault’s work from the disciplinary model to the governmentality model. Foucault’s work on power emerges within the general context of an expression of capitalist rationality and the nature of freedom and power within it. I argue that, thus understood, Foucault’s transition to the governmentality model can be seen simultaneously as a deepening recognition of what capitalism is and how it works, but also as a recognition of (...) the changing historical nature of the actually existing capitalisms and their specifically situated historical needs. I then argue that the disciplinary model should be understood as a contingent response to the demands of early capitalism, and argue that with the maturation of the capitalist enterprise many of those responses are no longer necessary. New realities require new responses; although this does not necessarily result in the abandonment of the earlier disciplinary model, it does require their reconfiguration according to the changed situation and the new imperatives following from it. (shrink)

Statistical fairness criteria are widely used for diagnosing and ameliorating algorithmic bias. However, these fairness criteria are controversial as their use raises several difficult questions. I argue that the major problems for statistical algorithmic fairness criteria stem from an incorrect understanding of their nature. These criteria are primarily used for two purposes: first, evaluating AI systems for bias, and second constraining machine learning optimization problems in order to ameliorate such bias. The first purpose typically involves treating each criterion (...) as a necessary condition for fairness. The second use involves treating criteria as sufficient conditions for fairness. Since the criteria are used for both roles, some researchers have treated them as both necessary and sufficient conditions, i.e., as definitions of algorithmic fairness. However, serious problems have been raised for the use of these fairness criteria. Under ordinary circumstances, it is impossible to satisfy multiple criteria at the same time. Moreover, there are counterexamples to both the sufficiency and necessity for fairness of each criterion. I argue that we should instead understand fairness criteria as merely providing evidence of fairness. In other words, satisfaction (or violation) of these criteria should be understood as potential evidence of fairness (or bias). Whether a criterion counts as evidence in a particular case will depend on stakeholders' background knowledge and the specific features of the system's task. This evidence account of fairness conditions provides guidance for recognizing both the appropriate uses and the limitations of fairness criteria. (shrink)

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. Using (...) these case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core. (shrink)

As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered (...) by conflations of various understandings of bias, ranging from neutral deviations from a standard to morally problematic instances of injustice due to prejudice, discrimination, and disparate treatment. This terminological confusion impedes efforts to address clear cases of discrimination. -/- In this paper, we examine the promises and challenges of different approaches to disambiguating bias and designing for justice. While both approaches aid in understanding and addressing clear algorithmic harms, we argue that they also risk being leveraged in ways that ultimately deflect accountability from those building and deploying these systems. Applying this analysis to recent examples of generative AI, our argument highlights unseen dangers in current methods of evaluating algorithmic bias and points to ways to redirect approaches to addressing bias in generative AI at its early stages in ways that can more robustly meet the demands of justice. (shrink)

Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key (...) ways in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- (...) ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research. (shrink)

We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic pluralism describes a state of affairs in which no set of algorithms severely limits access to opportunity, allowing individuals the freedom to pursue a diverse range of life paths. To argue for algorithmic pluralism, we adopt Joseph Fishkin's theory of bottlenecks, which focuses on the structure of decision-points that determine how opportunities are allocated. The theory contends that each (...) decision-point or bottleneck limits access to opportunities with some degree of severity and legitimacy. We extend Fishkin's structural viewpoint and use it to reframe existing systemic concerns about equal opportunity in algorithmic decision-making, such as patterned inequality and algorithmic monoculture. In proposing algorithmic pluralism, we argue for the urgent priority of alleviating severe bottlenecks in algorithmic decision-making. We contend that there must be a pluralism of opportunity available to many different individuals in order to promote equal opportunity in a systemic way. We further show how this framework has several implications for system design and regulation through current debates about equal opportunity in algorithmic hiring. (shrink)

Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice. However, such algorithmic decision systems can have prevalent biases against marginalized social groups and undermine social justice. Explainable artificial intelligence (XAI) is a recent development aiming to make an AI system’s decision processes less opaque and to expose its problematic biases. This paper argues against technical XAI, according to which the detection and interpretation of algorithmic bias can (...) be handled more or less independently by technical experts who specialize in XAI methods. Drawing on resources from feminist epistemology, we show why technical XAI is mistaken. Specifically, we demonstrate that the proper detection of algorithmic bias requires relevant interpretive resources, which can only be made available, in practice, by actively involving a diverse group of stakeholders. Finally, we suggest how feminist theories can help shape integrated XAI: an inclusive social-epistemic process that facilitates the amelioration of algorithmic bias. (shrink)

It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only (...) demonstrate how the philosophical conceptual framework of epistemic injustice can help pinpoint potential, systematic harms from algorithmic profiling, but we also identify a novel source of hermeneutical injustice that to date has received little attention in the relevant literature, what we call epistemic fragmentation. As we detail in this paper, epistemic fragmentation is a structural characteristic of algorithmically-mediated environments that isolate individuals, making it more difficult to develop, uptake and apply new epistemic resources, thus making it more difficult to identify and conceptualise emerging harms in these environments. We thus trace the occurrence of hermeneutical injustice back to the fragmentation of the epistemic experiences of individuals, who are left more vulnerable by the inability to share, compare and learn from shared experiences. (shrink)

Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of (...) false positives be equal across the relevant groups. We might seek to ensure that algorithms satisfy all of these purported fairness criteria. But a series of impossibility results shows that this is impossible, unless base rates are equal across the relevant groups. What are we to make of these pessimistic results? I argue that none of the purported criteria, except for a calibration criterion, are necessary conditions for fairness, on the grounds that they can all be simultaneously violated by a manifestly fair and uniquely optimal predictive algorithm, even when base rates are equal. I conclude with some general reflections on algorithmic fairness. (shrink)

The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we will (...) call the “Simple Stakes Thesis”. The Simple Stakes Thesis, as it will turn out, is too simple. For even if the stakes associated with a specific one-off decision are low—and hence does not engender a right to an explanation—such decisions may nevertheless form part of a high stakes pattern or aggregate of decisions. In such cases, we argue, even a low stakes decision may engender a right to explanation. Not only does this show that the right to explanation is more demanding than so far recognized. It also shows that the stakes thesis is significantly harder to apply in practice. (shrink)

This paper addresses the "algorithmic fine-tuning problem": why does our universe exhibit quantum mechanics if quantum mechanics is algorithmically improbable on a classical substrate? Building on our trilogy establishing the impossibility of deriving quantum structure (Axiom A1) from classical computation, we propose the Substrate Hypothesis: the universe's computational substrate is "quantum-native." We extend Chaitin's halting probability Ω from a real scalar to a state vector |Ω_Q⟩ in Hilbert space---the wavefunction of the algorithmic multiverse. We prove its normalizability (Theorem (...) 1) and establish a direct connection to classical Ω through the halting projection (Theorem 2). Using a minimal model language A1---named after the axiom it natively implements---we measure description length asymmetry: quantum operations require 25× fewer tokens on average (up to 40× at bit level) on a quantum substrate compared to classical NumPy implementations. This corresponds to an algorithmic probability gain of 2^878. We validate these theoretical predictions experimentally on AWS Braket, achieving >97% fidelity across all benchmarks with only 2--6 A1 tokens. This provides engineering validation of the Substrate Hypothesis: quantum mechanics is algorithmically natural on a quantum substrate. (shrink)

Algorithms wield increasing power over our lives. They can and often do wield that power unfairly, and much has been said about algorithmic fairness. In contrast, algorithmic neutrality has been largely neglected. I investigate algorithmic neutrality, asking: What is it? Is it possible? And what is its normative significance?

To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes. Using US mortgage lending as an example (...) use case, we discuss the ethical foundations of each definition of fairness and demonstrate that our proposed methodology more closely captures the ethical trade-offs of the decision-maker, as well as forcing a more explicit representation of which values and objectives are prioritised. (shrink)

In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not (...) consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. (shrink)

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...) variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research. (shrink)

This introduction to a special section on algorithmic thought provides a framework through which the articles in that collection can be contextualised and their individual contributions highlighted. Over the past decade, there has been a growing interest in artificial intelligence (AI). This special section reflects on this AI boom and its implications for studying what thinking is. Focusing on the algorithmic character of computing machines and the thinking that these machines might express, each of the special section’s essays (...) considers different dimensions of algorithmic thought, engaging with a diverse set of epistemological questions and issues. (shrink)

Does algorithmic political bias contribute to an entrenchment and polarization of political positions? Franke argues that it may do so because the bias involves classifications of people as liberals, conservatives, etc., and individuals often conform to the ways in which they are classified. I provide a novel example of this phenomenon in human–computer interactions and introduce a social psychological mechanism that has been overlooked in this context but should be experimentally explored. Furthermore, while Franke proposes that algorithmic political (...) classifications entrench political identities, I contend that they may often produce the opposite result. They can lead people to change in ways that disconfirm the classifications. Consequently and counterintuitively, algorithmic political bias can in fact decrease political entrenchment and polarization. (shrink)

The prospective introduction of autonomous cars into public traffic raises the question of how such systems should behave when an accident is inevitable. Due to concerns with self-interest and liberal legitimacy that have become paramount in the emerging debate, a contractarian framework seems to provide a particularly attractive means of approaching this problem. We examine one such attempt, which derives a harm minimisation rule from the assumptions of rational self-interest and ignorance of one’s position in a future accident. We contend, (...) however, that both contractarian approaches and harm minimisation standards are flawed, due to a failure to account for the fundamental difference between those ‘involved’ and ‘uninvolved’ in an impending crash. Drawing from classical works on the trolley problem, we show how this notion can be substantiated by reference to either the distinction between negative and positive rights, or to differences in people’s claims. By supplementing harm minimisation with corresponding constraints, we can develop crash algorithms for autonomous cars which are both ethically adequate and promise to overcome certain significant practical barriers to implementation. (shrink)

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and (...) fairness in healthcare. In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

In Algorithms & Autonomy, Rubel, Castro, and Pham (hereafter RCP), argue that the concept of autonomy is especially central to understanding important moral problems about algorithms. In particular, autonomy plays a role in analyzing the version of social contract theory that they endorse. I argue that although RCP are largely correct in their diagnosis of what is wrong with the algorithms they consider, those diagnoses can be appropriated by moral theories RCP see as in competition with their autonomy based theory. (...) Most notably, proponents of consequentialism and virtue ethics can appropriate RCPs insights. This is good news because RCP’s social contract theory is vulnerable to a well known class of counterexamples. The most significant contribution of RCP, if I am right, is in their identification, presentation, and evaluation of concrete cases involving algorithms and not in the more controversial claims about theoretical ethics that RCP themselves see as central to what they are doing. (shrink)

I argue that the thoroughly algorithmic nature of current AI systems (such as LLMs) is no obstacle to their being conscious. To this end, I present a picture on which current AI systems comprise dispositional properties which realize categorical phenomenal properties where the latter, in turn, provide the identity conditions for their dispositional realizers. This mutual ontological dependence, or, symmetrical grounding, at the heart of the proposal yields a novel picture of (AI) consciousness that avoids epiphenomenalism and is more (...) permissive regarding the specific nature/functional organization of conscious systems than has been previously suggested. This, in turn, suggests an epistemology of AI consciousness focused on investigating the high-level behaviours of AI systems rather than their low-level functional organization. (shrink)

In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive nature and function of a third-party audit, and the uncertain and shifting regulatory landscape, we suggest that second-party assessments are currently the primary mechanisms for analyzing the social impacts of systems that incorporate artificial intelligence. We then discuss two kinds of (...) as-sessments: an ethical risk assessment and a narrower, technical algo-rithmic bias assessment. We explain how the two assessments depend on each other, highlight the importance of situating the algorithm within its particular socio-technical context, and discuss a number of lessons and challenges for algorithm assessments and, potentially, for algorithm audits. The discussion builds on our team’s experience of advising and conducting ethical risk assessments for clients across dif-ferent industries in the last four years. Our main goal is to reflect on the key factors that are potentially ethically relevant in the use of algo-rithms, and draw lessons for the nascent algorithm assessment and audit industry, in the hope of helping all parties minimize the risk of harm from their use. (shrink)

This paper exposes how excessive reliance on either “efficiency-based algorithms,” which aim at rapid and accurate problem-solving, or highly addictive “randomized engagement- oriented algorithms,” which continuously distract individuals from being immersed in the present, induces a high level of conformity and thereby renders genuine wandering impos- sible, hindering human maturation. This paper names the current tendency that eliminates negativity—such as failure, pain, the capacity to resist uncertainty and stimulation—and enforces only positivity—such as achievement, pleasure, stability, and the immediate sat- isfaction (...) of stimulation—as “achievement perfectionism.” On the basis of Byung-Chul Han’s critique, who diagnoses such a society as The Palliative Society, this paper reveals that wandering accompanied by negativity and resistance to conformity is indispensable for human maturation. As a counter-concept to The Palliative Society and “achievement perfectionism,” the paper turns to Stanley Cavell’s (Cities of words, Harvard University Press, Cambridge, 2004) notion of “moral perfectionism,” which leads to “an unattained but attainable self” who can embrace negativity and explore the “rejected and underval- ued” paths with conformity’s aversion and openness. Finally, this paper, on the basis of Jan Masschelein’s (2010) poor pedagogy, focuses on the “attentive wandering” in which “moral perfectionism” can be educationally realized. Furthermore, this paper proposes an “educating of the gaze” through which individuals in the digital age, as “digital flaneurs,” can internalize the practice of genuine wandering that pays attention—thinking about what to see and how to respond to it—not only through the experience of bodily wandering by walking, but also by breaking away from the “pseudo-wandering” within algorithms. (shrink)

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...) concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms. (shrink)

In the age of streaming, we face a pressing problem of aesthetic choice: how are we to navigate the overwhelming quantity of content to which we now have access? Streaming platforms like Spotify and Netflix apply sophisticated machine learning tools to recommend personalized content to individual users. These recommender systems are presented to users as a technological solution to the problem of aesthetic choice, promising to help us discover new opportunities for engagement with aesthetic value. However, overreliance on algorithmic (...) recommendation can lead to diminished aesthetic autonomy, reduced aesthetic exploration, and an overall reduction in the satisfaction we take in aesthetic engagement. This is largely due to the fact that, in their most popular implementations, recommender systems are optimized to captivate users. They aim to maximize our engagement by recommending both content and modes of engagement which are initially attractive and frictionless, but which ultimately reshape our aesthetic personalities in ways that anchor us to streaming platforms. This paper explores these concerns before concluding with an assessment of whether it’s possible to use recommender systems in ways more conducive to our aesthetic flourishing. (shrink)

Algorithm Decision Making (ADM) systems designed to augment or automate human decision-making have the potential to produce better decisions while also freeing up human time and attention for other pursuits. For this potential to be realised, however, algorithmic decisions must be sufficiently aligned with human goals and interests. We take a Principal-Agent (P-A) approach to the questions of ADM alignment and trust. In a broad sense, ADM is beneficial if and only if human principals can trust algorithmic agents (...) to act faithfully on their behalf. This mirrors the challenge of facilitating P-A relationships among humans, but the peculiar nature of human-machine interaction also raises unique issues. The problem of asymmetric information is omnipresent but takes a different form in the context of ADM. Although the decision-making machinery of an algorithmic agent can in principle be laid bare for all to see, the sheer complexity of ADM systems based on deep learning models prevents straightforward monitoring. We draw on literature from economics and political science to argue that the problem of trust in ADM systems should be addressed at the level of institutions. Although the dyadic relationship between human principals and algorithmic agents is our ultimate concern, cooperation at this level must rest against an institutional environment which allows humans to effectively evaluate and choose among algorithmic alternatives. (shrink)

We cooperate with other people despite the risk of being exploited or hurt. If future artificial intelligence (AI) systems are benevolent and cooperative toward us, what will we do in return? Here we show that our cooperative dispositions are weaker when we interact with AI. In nine experiments, humans interacted with either another human or an AI agent in four classic social dilemma economic games and a newly designed game of Reciprocity that we introduce here. Contrary to the hypothesis that (...) people mistrust algorithms, participants trusted their AI partners to be as cooperative as humans. However, they did not return AI's benevolence as much and exploited the AI more than humans. These findings warn that future self-driving cars or co-working robots, whose success depends on humans' returning their cooperativeness, run the risk of being exploited. This vulnerability calls not just for smarter machines but also better human-centered policies. (shrink)

Is Artificial Intelligence the new 'Imam' that demands our blind submission? This paper diagnoses a contemporary crisis of intellectual paralysis, recasting the reliance on AI not as mere technological convenience, but as "Digital Taqlid"—a dangerous form of epistemic surrender. By placing Alan Turing’s "pedagogy of compliance" on trial against Al-Ghazali’s 11th-century critique of blind imitation, the study exposes how modern computation equates intelligence with refined mimicry, effectively stripping humans of their epistemic agency. We are no longer thinking; we are engaging (...) in "cognitive offloading" that mirrors the dogmatism of the past. As a corrective, this paper weaponizes Al-Ghazali’s concept of Taḥqīq (verification through existential struggle) as the only viable resistance. It argues that to survive the age of algorithmic authority, we must abandon the comfort of passive answers and reclaim the burden of authentic thought. (shrink)

This essay analyses those statements that mention legal norms in negative terms. Specifically, it analyses those statements that define a legal system by mentioning how legal protection does not work and where legal protection ends, and those statements that identify what rights‐holders do not have to with their legally protected free capacities. This essay argues that these statements address a systemic question. It calls such a dynamic as negative governmentality. The argument proceeds in four steps. It introduces the concept (...) of negative governmentality by arguing that the idea of freedom requires both the positive affirmation of moral agency and the constraining of moral agency (Section 2). It then explores how rights constitute freedom by limiting rights or making exceptions to them (Section 3). Later, it analyses how rights‐based norms prevent abuse of rights by holders of rights (Section 4). Finally, it sees how rights‐based norms constrain the legal guarantor of rights, i.e., a state (Section 5). The essay concludes by mentioning the importance of negative governmentality (Section 6). (shrink)

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to (...) explain why this systemic exclusion is of moral concern and to offer a solution to address it. (shrink)

The paper offers a solution to the generality problem for a reliabilist epistemology, by developing an “algorithm and parameters” scheme for type-individuating cognitive processes. Algorithms are detailed procedures for mapping inputs to outputs. Parameters are psychological variables that systematically affect processing. The relevant process type for a given token is given by the complete algorithmic characterization of the token, along with the values of all the causally relevant parameters. The typing that results is far removed from the typings of (...) folk psychology, and from much of the epistemology literature. But it is principled and empirically grounded, and shows good prospects for yielding the desired epistemological verdicts. The paper articulates and elaborates the theory, drawing out some of its consequences. Toward the end, the fleshed-out theory is applied to two important case studies: hallucination and cognitive penetration of perception. (shrink)

The aim of the research is to shed light on the nature of the organizational structure prevailing in Palestinian governmental universities and to identify the most important differences in the perceptions of employees of the organizational structure in the Palestinian governmental universities according to the demographic and organizational variables. The researchers used the descriptive analytical method, through a questionnaire randomly distributed to the sample of the employees of Al-Aqsa University. The study was conducted on a sample of (80) administrative staff (...) from Al-Aqsa University. The study found that there is a moderate degree of satisfaction with the nature of the organizational structure prevailing in the Palestinian governmental universities from the point of view of the administrative staff, with a percentage of (63.11%). The absence of differences between the sample according to the gender variable in their perception of the nature of the organizational structure prevailing at Al-Aqsa University, the absence of differences in their perception of the nature of the organizational structure depending on the age variable. There are statistical significance differences in the perception of the elements of the organizational climate depending on the variable of scientific qualification in their perception of the nature of the organizational structure in favor of holders of a bachelor's degree, the absence of differences in their perception of the nature of the organizational structure depending on the variable years of service, and the absence of differences in their perception of the nature of the organizational structure depending on the variable level of career (Director, Head of Department, and Administrative Officer). The study reached a number of recommendations, the most important of which is that the management of the Palestinian governmental universities in general and Al-Aqsa University in particular should be given special attention to the organizational structure and modified in a way that achieves the goals of the university and the aspirations of the employees. The universities should have the opportunity to participate in the restructuring of the organizational structure, the importance of solving the problems of employees and giving them the opportunity to contribute to solving their own problems, and the need to use the method of rotation of employees and periodically. (shrink)

We argue that machine learning algorithms can inflict microaggressions on members of marginalized groups and that recognizing these harms as instances of microaggressions is key to effectively addressing the problem. The concept of microaggression is also illuminated by being studied in algorithmic contexts. We contribute to the microaggression literature by expanding the category of environmental microaggressions and highlighting the unique issues of moral responsibility that arise when we focus on this category. We theorize two kinds of algorithmic microaggression, (...) stereotyping and erasure microaggressions, and argue that corporations are responsible for the microaggressions their algorithms create. As a case study, we look at the problems faced by Google’s autocomplete prediction and at the insufficiency of their solutions. The case study of autocomplete demonstrates our core claim that microaggressions constitute a distinct form of algorithmic bias and that identifying them as such allows us to avoid seeming solutions that recreate the same kinds of harms. Google has a responsibility to make information freely available, without exposing users to degradation. To fulfill its duties to marginalized groups, Google must abandon the fiction of neutral prediction and instead embrace the liberatory power of suggestion. (shrink)

Law-enforcement agencies are increasingly able to leverage crime statistics to make risk predictions for particular individuals, employing a form of inference that some condemn as violating the right to be “treated as an individual.” I suggest that the right encodes agents’ entitlement to a fair distribution of the burdens and benefits of the rule of law. Rather than precluding statistical prediction, it requires that citizens be able to anticipate which variables will be used as predictors and act intentionally to avoid (...) them. Furthermore, it condemns reliance on various indexes of distributive injustice, or unchosen properties, as evidence of law-breaking. (shrink)

We apply recent ideas about complexity and randomness to the philosophy of laws and chances. We develop two ways to use algorithmic randomness to characterize probabilistic laws of nature. The first, a generative chance* law, employs a nonstandard notion of chance. The second, a probabilistic* constraining law, impose relative frequency and randomness constraints that every physically possible world must satisfy. The constraining notion removes a major obstacle to a unified governing account of non-Humean laws, on which laws govern by (...) constraining physical possibilities; it also provides independently motivated solutions to familiar problems for the Humean best-system account (the Big Bad Bug and the zero-fit problem). On either approach, probabilistic laws are tied more tightly to corresponding sets of possible worlds: some histories permitted by traditional probabilistic laws are now ruled out as physically impossible. Consequently, the framework avoids one variety of empirical underdetermination while bringing to light others that are typically overlooked. (shrink)

As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments from feminist philosophy of (...) science to machine learning programs to make the case that the resources required to respond to these inductive challenges render critical aspects of their design constitutively value-laden. I demonstrate these points specifically in the case of recidivism algorithms, arguing that contemporary debates concerning fairness in criminal justice risk-assessment programs are best understood as iterations of traditional arguments from inductive risk and demarcation, and thereby establish the value-laden nature of automated decision-making programs. Finally, in light of these points, I address opportunities for relocating the value-free ideal in machine learning and the limitations that accompany them. (shrink)