The goal of this effort is to map the landscape of uses, risks, and harms associated with genetic data, and make recommendations for the public and the policymakers on how to govern this sensitive data. The relevance of our work is underscored by the recent 23andMe data breach, which impacted nearly 7 million users, and the growing movement in state legislatures to integrate genetic data protections into privacy laws.

Direct-to-Consumer Genetic Testing: Data Flow, Governance, and Recommendations to Mitigate Harm | Undergraduate Thesis by Amit Levi

We undertake research that seeks to build better evaluation for machine learning models, in theory and in practice.

• The Misuse of AUC: What High Impact Risk Assessment Gets Wrong | Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
• Don't Let the Math Distract You: Together, We Can Fight Algorithmic Injustice | ACLU
• To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models  | Proc. AI and Statistics, 2024.
• Observing Context Improves Disparity Estimation when Race is Unobserved | AI, Ethics, and Society, 2024.

We undertake research on numerous questions at the intersection of technology, law, and policy. These include understanding the role of generative AI in copyright, examining how privacy-enhancing technologies might subvert data protection goals, exploring how the technical and legal discussions around data minimization are often at odds, and identifying points of commonality and difference between generative and predictive AI.

• Break It 'Til You Make It: An Exploration of the Ramifications of Copyright Liability Under a Pre-training Paradigm of AI Development
• You Still See Me: How Data Protection Supports the Architecture of ML Surveillance
• Compliance Cards: Computational Artifacts for Automated AI Regulation Compliance
• Deconstructing Design Decisions: Why Courts Must Interrogate Machine Learning and Other Technologies