Ethical Innovation Standards

Check out our new piece in Nature entitled: "We Need a New Ethics for a World of AI Agents" https://lnkd.in/eSwJCrKu AI is undergoing a profound ‘agentic turn’—shifting from passive tools to autonomous actors in our world. This moment demands a new ethical framework. With Geoff Keeling, Arianna Manzini, PhD (Oxon) & James Evans and the team at Google DeepMind/Google, we focus on two core challenges. 1️⃣ The Alignment Problem: When agents can act in the world, the consequences of misaligned goals become tangible and immediate. 2️⃣ Social Agents: Their ability to form deep, long-term relationships with users introduces new risks of emotional harm. To address this, we must expand our conception of value alignment: It's not enough for an AI agent to simply follow commands. It must also align with broader principles: User well-being, long-term flourishing, and societal norms. For social agents, we argue for an ethics of care: They must be designed to respect user autonomy and serve as a complement—not a surrogate—for a flourishing human life. Moving forward requires proactive stewardship of the entire AI agent ecosystem. This means more realistic evaluations, governance that keeps pace with capabilities, and industry collaboration to ensure this future is safe and human-centric 👍

Not every design principle should make your product more engaging. Some should protect people. You’ve probably seen Laws of UX, but its creator, Jon Yablonski also runs another brilliant project: humanebydesign.com It’s a framework for building digital products that respect users, not just attract them. Core principles: 1. Resilient → Design for the most vulnerable and anticipate misuse 2. Empowering → Centre on the value products provide to people  3. Finite → Respect people’s time and focus on meaningful content 4. Inclusive → Reflect the full range of human diversity 5. Intentional → Add friction where needed and favour long-term well-being 6. Respectful → Protect attention and digital health 7. Transparent → Be honest, clear, and free of dark patterns Honestly, I teach and implement this way too little myself, still stuck very much in the optimisation game. So this isn’t preaching, it’s sharing. And as usual with Yablonski’s work, the site is beautifully crafted, full of thoughtful illustrations and links to in-depth articles and research on each principle. So dive in, enjoy, just as I will!

🚨 If you're interested in AI agents, "Resist Platform-Controlled AI Agents and Champion User-Centric Agent Advocates," by Sayash Kapoor, Noam Kolt & Seth Lazar, is the visionary paper you should be reading today: "Computing amplifies agency. In the hands of the powerful, it reinforces centralized control. In the hands of individuals, it can enable counter-power. Historically, there have been recurrent moments of technological expansion that seemed poised to usher in a more decentralized computing future. Each time, however, centralizing forces have reasserted themselves. Examples abound: the first hackers circumventing the gatekeepers of MIT’s PDP-6; the Silicon Valley Homebrew Club building alternatives to IBM’s mainframes; open, customizable software vs. closed operating systems; community-run BBSs vs centralized ISPs; the open internet standing against the internet of platforms, and more. Our current moment is not unique. It may, however, present a unique opportunity. Previously, the pathway toward decentralization was accessible primarily to technologically skilled users—hackers capable of circumventing constraints set by centralized authorities. Today, however, user-centric agent advocates could level the playing field. By default, the trajectory of agent-based AI systems is likely to follow the same centralized pattern as the platform economy. Incumbent and aspiring platform companies will develop and control powerful agentic systems. These platform agents will intermediate digital interactions across countless personal and professional contexts. Although users may guide platform agents, ultimate control will remain firmly with centralized developers. Platform-controlled AI agents will be double agents, with the potential for profoundly negative implications: heightened surveillance, constrained user choice, granular market manipulation, and broad illegitimate power. The worst of platform capitalism’s current ills could be exacerbated. But this outcome is not inevitable. A compelling alternative exists: user-centric agent advocates designed to serve the interests of individual users, not platform companies. Representatives, not go-betweens, that reject platform logic. Agent advocates could provide a path to harnessing the promise of AI agents without succumbing to platform-based control. Realizing this decentralized alternative will require targeted technical and institutional interventions. These include ensuring the availability of open-source models and public computational resources, as well as establishing robust safety standards and governance frameworks. It will also require engineers who can build highly capable universal intermediaries but resist entering the race to create the next platform. Independent researchers and developers must prioritize addressing these challenges now—before the default pathway locks in." 👉 Link below. 👉 Never miss my updates: join my newsletter's 61,200+ subscribers below.

With 30 years of experience in the technology sector, including in engineering & operations, I’ve developed my own best practices that help organizations build trust with the communities who will use their technology.  In this week’s special TIME Magazine Davos issue, I outlined a framework based on those hard-won lessons to help ensure AI development is responsible, thoughtful, and benefits humanity, including: - Embrace Early Collaboration: Bringing outside voices into the development process early helps to create technology that better reflects the breadth and depth of the human experience. Ensuring you partner with - and listen to - experts & local communities can help mitigate potential risks. - Operationalize Care: The success of AI projects often hinges on how well organizations implement systems that operationalize their commitment to care. For example, at Google DeepMind, we have developed frameworks that embed ethical considerations and safety measures into the fabric of any research and development process - as fundamental building blocks, not bolted-on afterthoughts. - Build Trust Through Real-World Impact: The antidote to apprehension around AI is to build products that solve real problems, and then highlight those solutions. When people understand how AI is adding clear value to their lives, the conversation can focus both on positive  opportunities and managing risk. I very much appreciated the opportunity to share my thoughts, and you can read more here:

Do you think about ethics when you use AI? Most AI ethics discussions focus on regulation, but it’s important to consider how AI *users* are incorporating their ethics into their AI usage. In this week’s #LeadingDisruption, I share my values-in-action model for AI ethics. It’s a practical framework for embedding your personal and organizational values directly into your AI workflows. I walk you through exactly how I apply my core values (openness, curiosity, integrity, humility) to my AI use, plus give you actionable steps to start this week. Because technology may be shaping our future, but it's our values that shape technology.

Humanizing AI Through the Kano Model In an era where generative AI has become a ubiquitous offering, true differentiation lies not in merely adopting the technology but in integrating human values into its core. Building on my earlier discussion about applying the Kano Model to Gen AI strategy, let’s explore how this framework can refocus development metrics to prioritize ethics and human-centricity. By aligning AI systems with human needs, organizations can shift from functional tools to trusted partners that inspire lasting loyalty. Traditional metrics such as speed, scalability, and model accuracy have evolved into basic expectations the “must-haves” of AI. What truly elevates a product today is its ability to embody values like safety, helpfulness, dignity, and harmlessness. These qualities, categorized as “delighters” in the Kano Model, transform AI from a transactional tool into a meaningful collaborator. Key Human-Centric Differentiators Safety: Proactive safeguards must ensure AI systems protect users from risks, whether physical, emotional, or societal. Safety is non-negotiable in building trust. Helpfulness: Personalized, context-aware interactions demonstrate empathy. AI should anticipate needs and adapt to individual preferences, turning routine tasks into meaningful experiences. Dignity: Ethical design principles—fairness, transparency, and privacy—must underpin AI development. Respecting user autonomy fosters long-term trust and engagement. Harmlessness: AI outputs and recommendations should prioritize user well-being, avoiding unintended consequences like bias, misinformation, or psychological harm. This human-centered approach represents a paradigm shift in technology development. While traditional KPIs remain important, they are no longer sufficient to stand out in a crowded market. Organizations that embed human values into their AI systems will not only meet user expectations but exceed them, creating emotional connections that drive loyalty. By applying the Kano Model, businesses can systematically align innovation with ethics, ensuring technology serves humanity rather than the other way around. The future of AI isn’t just about efficiency it’s about elevating human potential through thoughtful, responsible design. How is your organization balancing technical excellence with human values?

As AI advances apace, potentially beyond "Slave AI", framing and designing "Friendly AI" may be our best approach. A comprehensive review article on the space uncovers the foundations, pros and cons, applications, and future directions for the space. The paper defines Friendly AI (FAI) as "an initiative to create systems that not only prioritise human safety and well-being but also actively foster mutual respect, understanding, and trust between humans and AI, ensuring alignment with human values and emotional needs in all interactions and decisions." It intends to go beyond existing anthropocentric frameworks. Key insights in the review paper from include: 🔄 Balance Ethical Frameworks and Practical Feasibility. The development of FAI relies on integrating ethical principles like deontology, value alignment, and altruism. While these frameworks provide a moral compass, their operationalization faces challenges due to the evolving nature of human values and cultural diversity. 🌍 Address Global Collaboration Barriers. Developing FAI requires global cooperation, but diverging ethical standards, regulatory priorities, and commercial interests hinder alignment. Establishing international platforms and shared frameworks could harmonize these efforts across nations and industries. 🔍 Enhance Transparency with Explainable AI. Explainable AI (XAI) techniques like LIME and SHAP empower users to understand AI decisions, fostering trust and enabling ethical oversight. This transparency is foundational to FAI’s goal of aligning AI behavior with human expectations. 🔐 Build Trust Through Privacy Preservation. Privacy-preserving methods, such as federated learning and differential privacy, protect user data and ensure ethical compliance. These approaches are critical to maintaining user trust and upholding FAI's values of dignity and respect. ⚖️ Embed Fairness in AI Systems. Fairness techniques mitigate bias by addressing imbalances in data and outputs. Ensuring equitable treatment of diverse groups aligns AI systems with societal values and supports FAI’s commitment to inclusivity. 💡 Leverage Affective Computing for Empathy. Affective Computing (AC) enhances AI’s ability to interpret human emotions, enabling empathetic interactions. AC is pivotal in healthcare, education, and robotics, bridging human-AI communication for more "friendly" systems. 📈 Focus on ANI-AGI Transition Challenges. Advancing AI capabilities in nuanced decision-making, memory, and contextual understanding is crucial for transitioning from narrow AI (ANI) to general AI (AGI) while maintaining alignment with FAI principles. 🤝 Foster Multi-Stakeholder Collaboration. FAI’s realization demands structured collaboration across governments, academia, and industries. Clear guidelines, shared resources, and public inclusion can address diverging goals and accelerate FAI’s adoption globally. Link to paper in comments

Everywhere I look right now, leaders are being told to move fast on AI. Automate more. Do more with less. Outpace the competition. But the more I listen to teams, the clearer it becomes: the real risk isn’t AI itself, it’s deploying AI in our workplaces without shared values, guardrails, or conversation. A recent article on AI ethics in the workplace highlights how many organizations are rolling out AI tools with little governance around fairness, transparency, or accountability. That gap matters. And it doesn’t fall evenly. It often lands hardest on people already marginalized by biased data, opaque systems, and limited access to decision-making power. As a BIPOC woman founder who works at the intersection of workplace culture, equity, and leadership, I don’t see ethical AI as a “tech issue.” I see it as a leadership practice. Ethical AI requires leaders to slow down long enough to ask: 🙋🏽♀️ Who could be harmed by this decision? 🙋🏽♀️Who is missing from the room? 🙋🏽♀️How will people question, challenge, or opt out? These are not technical questions. They are culture questions. That’s why Curated Leadership is launching a new series of Ethical AI workshops in 2026. These workshops examines bias in technology, data privacy, and responsible AI adoption, and invites participants to work through concrete examples from hiring, performance, and everyday tools. Participants will practice applying an equity lens when integrating new AI tools and systems so that innovation does not come at the expense of fairness, trust, or psychological safety. What questions or concerns about AI are coming up most often in your workplace right now? Comment below 👇🏾 #EthicalAI #ResponsibleAI #AIandEquity #Leadership #WorkplaceCulture #FutureOfWork #PeopleFirst #Culture

In my latest column for Trellis Group, I’m delighted to collaborate with the brilliant Dave Lütkenhaus on how our hero-driven innovation models are old school, dangerous and counterproductive. “Predictions that AI ethicists will be in huge demand haven’t materialized so far — instead, there’s widespread concern about “over-regulation.” Similarly, green tech startups, hawking e-scooters to solar products, have emerged with revolutionary ideas, such as batteries relying on minerals mined under ethically questionable conditions, only to face backlash when their supply chains reveal human rights violations or environmental degradation. Such firms have tended to assume that their environmental license to operate is sufficient, which means they may have overlooked their community and social impacts from the beginning. This disconnect isn’t malicious, it’s systemic: Ethical questions only surface after prototypes launch. In most corporate innovation processes, there’s simply no forum or capability to consider them. As societal trust in business continues to disintegrate, the move-quickly-and-break-things model is becoming more obsolete. Instead, innovation and ethics must collaborate from the outset, not treat each other as afterthoughts. In a world of cascading risks and eroding trust, that is not just a moral imperative; it’s a competitive advantage.” We need to change the how, not just the what! https://lnkd.in/dDYDCNsN

𝗔𝗜 𝗘𝗠𝗢𝗧𝗜𝗢𝗡𝗔𝗟 𝗜𝗡𝗧𝗘𝗟𝗟𝗜𝗚𝗘𝗡𝗖𝗘: 𝗘𝗧𝗛𝗜𝗖𝗔𝗟 𝗦𝗔𝗙𝗘𝗚𝗨𝗔𝗥𝗗𝗦 𝗙𝗢𝗥 𝗠𝗔𝗡𝗜𝗣𝗨𝗟𝗔𝗧𝗜𝗢𝗡 As AI systems become more adept at recognizing and responding to human emotions, concerns are growing about how this emotional intelligence could be used to manipulate users. To counter this, ethical safeguards are being introduced to ensure emotional AI enhances well-being instead of exploiting vulnerabilities. 𝗦𝘁𝗲𝗽𝘀 𝗧𝗮𝗸𝗲𝗻: Developers are incorporating ethical design principles into emotionally intelligent AI to prevent manipulation and emotional exploitation. Some AI ethics frameworks now include guidelines for transparency, emotional neutrality, and respect for user autonomy. For instance, research institutions are advising against emotionally coercive AI in customer service, mental health apps, and virtual assistants. 𝗪𝗵𝗼 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱: AI ethics research labs such as the 𝗔𝗜 𝗡𝗼𝘄 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗲 and advocacy organizations like the 𝗖𝗲𝗻𝘁𝗲𝗿 𝗳𝗼𝗿 𝗛𝘂𝗺𝗮𝗻𝗲 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 have been pivotal in promoting ethical emotional AI. These groups highlight the need for boundaries when AI interacts with human emotions, encouraging developers to design systems that prioritize empathy over exploitation. 𝗛𝗼𝘄 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗛𝗲𝗹𝗽: 𝗔𝘀 𝗮 𝗖𝗼𝗺𝗽𝗮𝗻𝘆: • Design emotional AI systems that center user well-being and mental health. • Implement transparency in emotional data usage and avoid manipulative engagement tactics. 𝗔𝘀 𝗮𝗻 𝗜𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹: • Support emotionally intelligent technologies that are transparent and respectful. • Question emotional AI experiences that feel exploitative, and provide feedback to developers. 𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻: Emotional intelligence in AI can improve lives—but only if handled ethically. What safeguards do you think are essential to ensure emotionally aware AI respects human dignity? Stay tuned for next week’s post in this ongoing series, where we explore 𝗚𝗹𝗼𝗯𝗮𝗹 𝗔𝗜 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 𝗔𝗰𝗿𝗼𝘀𝘀 𝗕𝗼𝗿𝗱𝗲𝗿𝘀. #AI #Ethics #CourseCorrection #EmotionalAI #AIEthics #UserWellBeing #CosmosRevisits