Legal Implications of AI and Data Privacy

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

The rapid advancement of AI technologies, particularly LLMs, has highlighted important questions about the application of privacy laws like the GDPR. As someone who has been grappling with this issue for years, I am *thrilled* to see the Hamburg DPC's discussion paper approach privacy risks and AI with a deep understanding of the technology. A few absolutely refreshing takeaways: ➡ LLMs process tokens and vectorial relationships between tokens (embeddings), fundamentally differing from conventional data storage and retrieval. The Hamburg DPC finds that LLMs don't "process" or "store" personal data within the meaning of the GDPR. ➡ Unlike traditional identifiers, tokens and their embeddings in LLMs lack the necessary direct, targeted association to individuals that characterizes personal data in CJEU jurisprudence. ➡ Memorization attacks that extract training data from an LLM don't necessarily conclude that personal data is stored in the LLM. These attacks may be practically disproportionate and potentially legally prohibited, making personal identification not "possible" under the legislation. ➡ Even if personal data was unlawfully processed in developing the LLM, it doesn't render the use of the resulting LLM illegal (providing downstream deployers some comfort when leveraging third-party models). This is a nuanced and technology-informed perspective on the complex intersection of AI and privacy. As we continue to navigate this rapidly evolving landscape, I hope we see more regulators and courts approach regulation and legal compliance with a deep understanding of how the technology actually works. #AI #Privacy #GDPR #LLM

European Data Protection Board issues long awaited opinion on AI models: part 3 - anonymization (See Part 1: https://shorturl.at/TYbq3 consequences and Part 2: https://shorturl.at/ba5A1 legitimate interest legal basis). 🔹️AI models are not always anonymous; assess case by case. 🔹️ AI models specifically designed to provide personal data regarding individuals whose personal data were used to train the model, cannot be considered anonymous. 🔹️For an AI model to be considered anonymous, both (1) the likelihood of direct (including probabilistic) extraction of personal data regarding individuals whose personal data were used to develop the model and (2) the likelihood of obtaining, intentionally or not, such personal data from queries, should be insignificant, taking into account ‘all the means reasonably likely to be used’ by the controller or another person. 🔹️ Pay special attention to risk of singling out, which is substantial 🔹️ Consider all means reasonably likely to be used by the controller or another person to identify individuals which may include: characteristics of training data, AI model & training procedure; context; c. additional information; costs and amount of time needed to obtain such info; available technology & technological developments. 🔹️ Such means & levels of testing may differ between a publicly available and a model to be used only internally by employees. 🔹️ Consider risk of identification by controller & different types of ‘other persons’, including unintended third parties accessing the AI model, and unintended reuse or disclosure of model. Be able to prove, through steps taken and documentation, that you have taken effective measures to anonymize the AI Model. Otherwise, you may be in breach of your accountability obligations under Article 5(2) GDPR. Factors to consider: 🔹️ selection of sources: (selection criteria; relevance and adequacy of chosen sources; exclusion of inappropriate sources. 🔹️ preparation of data for training phase: (could you use anonymous or pseudonymous); if not why not; data minimisation strategies & techniques to restrict volume of personal data included in training process; data filtering processes to remove irrelevant personal data. 🔹️ Methodological choices regarding training: improve model generalisation & reduce overfitting; privacy-preserving techniques (e.g. differential privacy) 🔹️ Measures regarding outputs of model (lower likelihood of obtaining personal data related to training data from queries). 🔹️ Conduct sufficient tests on model that cover widely known, state-of-the-art attacks: eg attribute and membership inference; exfiltration; regurgitation of training data; model inversion; or reconstruction attacks. 🔹️ Document process including: DPIA; advice by DPO; technical & organisational measures; AI model’s theoretical resistance to re-identification techniques. #dataprivacy #dataprotection #privacyFOMO #AIFOMO Pic by Grok

🧠 “Data systems are designed to remember data, not to forget data.” – Debbie Reynolds, The Data Diva 🚨 I just published a new essay in the Data Privacy Advantage newsletter called: 🧬An AI Data Privacy Cautionary Tale: Court-Ordered Data Retention Meets Privacy🧬 🧠 This essay explores the recent court order from the United States District Court for the Southern District of New York in the New York Times v. OpenAI case. The court ordered OpenAI to preserve all user interactions, including chat logs, prompts, API traffic, and generated outputs, with no deletion allowed, not even at the user's request. 💥 That means: 💥“Delete” no longer means delete 💥API business users are not exempt 💥Personal, confidential, or proprietary data entered into ChatGPT could now be locked in indefinitely 💥Even if you never knew your data would be involved in litigation, it may now be preserved beyond your control 🏛️ This order overrides global privacy laws, such as the GDPR and CCPA, highlighting how litigation can erode deletion rights and intensify the risks associated with using generative AI tools. 🔍 In the essay, I cover: ✅ What the court order says and why it matters ✅ Why enterprise API users are directly affected ✅ How AI models retain data behind the scenes ✅ The conflict between privacy laws and legal hold obligations ✅ What businesses should do now to avoid exposure 💡 My recommendations include: • Train employees on what not to submit to AI • Curate all data inputs with legal oversight • Review vendor contracts for retention language • Establish internal policies for AI usage and audits • Require transparency from AI providers 🏢 If your organization is using generative AI, even in limited ways, now is the time to assess your data discipline. AI inputs are no longer just temporary interactions; they are potentially discoverable records. And now, courts are treating them that way. 📖 Read the full essay to understand why AI data privacy cannot be an afterthought. #Privacy #Cybersecurity #datadiva#DataPrivacy #AI #LegalRisk #LitigationHold #PrivacyByDesign #TheDataDiva #OpenAI #ChatGPT #Governance #Compliance #NYTvOpenAI #GenerativeAI #DataGovernance #PrivacyMatters

The Oregon Department of Justice released new guidance on legal requirements when using AI. Here are the key privacy considerations, and four steps for companies to stay in-line with Oregon privacy law. ⤵️ The guidance details the AG's views of how uses of personal data in connection with AI or training AI models triggers obligations under the Oregon Consumer Privacy Act, including: 🔸Privacy Notices. Companies must disclose in their privacy notices when personal data is used to train AI systems. 🔸Consent. Updated privacy policies disclosing uses of personal data for AI training cannot justify the use of previously collected personal data for AI training; affirmative consent must be obtained. 🔸Revoking Consent. Where consent is provided to use personal data for AI training, there must be a way to withdraw consent and processing of that personal data must end within 15 days. 🔸Sensitive Data. Explicit consent must be obtained before sensitive personal data is used to develop or train AI systems. 🔸Training Datasets. Developers purchasing or using third-party personal data sets for model training may be personal data controllers, with all the required obligations that data controllers have under the law. 🔸Opt-Out Rights. Consumers have the right to opt-out of AI uses for certain decisions like housing, education, or lending. 🔸Deletion. Consumer #PersonalData deletion rights need to be respected when using AI models. 🔸Assessments. Using personal data in connection with AI models, or processing it in connection with AI models that involve profiling or other activities with heightened risk of harm, trigger data protection assessment requirements. The guidance also highlights a number of scenarios where sales practices using AI or misrepresentations due to AI use can violate the Unlawful Trade Practices Act. Here's a few steps to help stay on top of #privacy requirements under Oregon law and this guidance: 1️⃣ Confirm whether your organization or its vendors train #ArtificialIntelligence solutions on personal data.  2️⃣ Validate your organization's privacy notice discloses AI training practices. 3️⃣ Make sure organizational individual rights processes are scoped for personal data used in AI training. 4️⃣ Set assessment protocols where required to conduct and document data protection assessments that address the requirements under Oregon and other states' laws, and that are maintained in a format that can be provided to regulators.

⚠️Privacy Risks in AI Management: Lessons from Italy’s DeepSeek Ban⚠️ Italy’s recent ban on #DeepSeek over privacy concerns underscores the need for organizations to integrate stronger data protection measures into their AI Management System (#AIMS), AI Impact Assessment (#AIIA), and AI Risk Assessment (#AIRA). Ensuring compliance with #ISO42001, #ISO42005 (DIS), #ISO23894, and #ISO27701 (DIS) guidelines is now more material than ever. 1. Strengthening AI Management Systems (AIMS) with Privacy Controls 🔑Key Considerations: 🔸ISO 42001 Clause 6.1.2 (AI Risk Assessment): Organizations must integrate privacy risk evaluations into their AI management framework. 🔸ISO 42001 Clause 6.1.4 (AI System Impact Assessment): Requires assessing AI system risks, including personal data exposure and third-party data handling. 🔸ISO 27701 Clause 5.2 (Privacy Policy): Calls for explicit privacy commitments in AI policies to ensure alignment with global data protection laws. 🪛Implementation Example: Establish an AI Data Protection Policy that incorporates ISO27701 guidelines and explicitly defines how AI models handle user data. 2. Enhancing AI Impact Assessments (AIIA) to Address Privacy Risks 🔑Key Considerations: 🔸ISO 42005 Clause 4.7 (Sensitive Use & Impact Thresholds): Mandates defining thresholds for AI systems handling personal data. 🔸ISO 42005 Clause 5.8 (Potential AI System Harms & Benefits): Identifies risks of data misuse, profiling, and unauthorized access. 🔸ISO 27701 Clause A.1.2.6 (Privacy Impact Assessment): Requires documenting how AI systems process personally identifiable information (#PII). 🪛 Implementation Example: Conduct a Privacy Impact Assessment (#PIA) during AI system design to evaluate data collection, retention policies, and user consent mechanisms. 3. Integrating AI Risk Assessments (AIRA) to Mitigate Regulatory Exposure 🔑Key Considerations: 🔸ISO 23894 Clause 6.4.2 (Risk Identification): Calls for AI models to identify and mitigate privacy risks tied to automated decision-making. 🔸ISO 23894 Clause 6.4.4 (Risk Evaluation): Evaluates the consequences of noncompliance with regulations like #GDPR. 🔸ISO 27701 Clause A.1.3.7 (Access, Correction, & Erasure): Ensures AI systems respect user rights to modify or delete their data. 🪛 Implementation Example: Establish compliance audits that review AI data handling practices against evolving regulatory standards. ➡️ Final Thoughts: Governance Can’t Wait The DeepSeek ban is a clear warning that privacy safeguards in AIMS, AIIA, and AIRA aren’t optional. They’re essential for regulatory compliance, stakeholder trust, and business resilience. 🔑 Key actions: ◻️Adopt AI privacy and governance frameworks (ISO42001 & 27701). ◻️Conduct AI impact assessments to preempt regulatory concerns (ISO 42005). ◻️Align risk assessments with global privacy laws (ISO23894 & 27701).   Privacy-first AI shouldn't be seen just as a cost of doing business, it’s actually your new competitive advantage.