Ellie Graeden | Searcharxiv

arXiv2024

Community-Informed AI Models for Police Accountability

Face-to-face interactions between police officers and the public affect both individual well-being and democratic legitimacy. Many government-public interactions are captured on video, including interactions between police officers and drivers captured on bodyworn cameras (BWCs). New advances in AI technology enable these interactions to be analyzed at scale, opening promising avenues for improving government transparency and accountability. However, for AI to serve democratic governance effectively, models must be designed to include the preferences and perspectives of the governed. This article proposes a community-informed, approach to developing multi-perspective AI tools for government accountability. We illustrate our approach by describing the research project through which the approach was inductively developed: an effort to build AI tools to analyze BWC footage of traffic stops conducted by the Los Angeles Police Department. We focus on the role of social scientists as members of multidisciplinary teams responsible for integrating the perspectives of diverse stakeholders into the development of AI tools in the domain of police -- and government -- accountability.

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arXiv2023

A new framework for global data regulation

Under the current regulatory framework for data protections, the protection of human rights writ large and the corresponding outcomes are regulated largely independently from the data and tools that both threaten those rights and are needed to protect them. This separation between tools and the outcomes they generate risks overregulation of the data and tools themselves when not linked to sensitive use cases. In parallel, separation risks under-regulation if the data can be collected and processed under a less-restrictive framework, but used to drive an outcome that requires additional sensitivity and restrictions. A new approach is needed to support differential protections based on the genuinely high-risk use cases within each sector. Here, we propose a regulatory framework designed to apply not to specific data or tools themselves, but to the outcomes and rights that are linked to the use of these data and tools in context. This framework is designed to recognize, address, and protect a broad range of human rights, including privacy, and suggests a more flexible approach to policy making that is aligned with current engineering tools and practices. We test this framework in the context of open banking and describe how current privacy-enhancing technologies and other engineering strategies can be applied in this context and that of contract tracing applications. This approach for data protection regulations more effectively builds on existing engineering tools and protects the wide range of human rights defined by legislation and constitutions around the globe.

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