The Deeper Inquiry

The AI alignment literature — the body of work concerned with how AI systems can be designed to pursue the objectives that their designers actually intend rather than the objectives that are technically specified — is directly relevant to Providence's AI governance design. Stuart Russell's Human Compatible (2019) provides the most accessible account of the alignment problem and the reasons it is a genuine problem rather than merely a theoretical concern. The work of the Center for Human-Compatible AI at Berkeley, the work of the Machine Intelligence Research Institute, and the work of the AI safety team at DeepMind all bear on the specific technical question of how AI constraints can be made robust.

The political philosophy of algorithmic governance has developed rapidly in the past decade. Frank Pasquale's The Black Box Society (2015) and New Laws of Robotics (2020), Virginia Eubanks' Automating Inequality (2018), Safiya Umoja Noble's Algorithms of Oppression (2018), and Kate Crawford's Atlas of AI (2021) collectively provide an extensive analysis of how AI systems embedded in existing power structures reproduce and amplify those structures' inequities. Providence's AI governance design must engage this literature seriously: the constitutional commitment to human dignity and participatory legitimacy is not satisfied by AI systems that are well-intentioned but whose operations reproduce existing hierarchies of disadvantage.

The emerging literature on AI auditing and accountability — including the work of the Algorithmic Justice League, the AI Now Institute, and the Partnership on AI — provides practitioner-oriented frameworks for how AI systems can be made accountable in institutional settings. The work is imperfect and the tools are incomplete, but they represent the current state of the art for the specific problem of making AI systems governable within institutions that are genuinely committed to doing so.