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Full-Text Articles in Science and Technology Law
Algorithmic Governance From The Bottom Up, Hannah Bloch-Wehba
Algorithmic Governance From The Bottom Up, Hannah Bloch-Wehba
Faculty Scholarship
Artificial intelligence and machine learning are both a blessing and a curse for governance. In theory, algorithmic governance makes government more efficient, more accurate, and more fair. But the emergence of automation in governance also rests on public-private collaborations that expand both public and private power, aggravate transparency and accountability gaps, and create significant obstacles for those seeking algorithmic justice. In response, a nascent body of law proposes technocratic policy changes to foster algorithmic accountability, ethics, and transparency.
This Article examines an alternative vision of algorithmic governance, one advanced primarily by social and labor movements instead of technocrats and firms. …
Transparency's Ai Problem, Hannah Bloch-Wehba
Transparency's Ai Problem, Hannah Bloch-Wehba
Faculty Scholarship
A consensus seems to be emerging that algorithmic governance is too opaque and ought to be made more accountable and transparent. But algorithmic governance underscores the limited capacity of transparency law—the Freedom of Information Act and its state equivalents—to promote accountability. Drawing on the critical literature on “open government,” this Essay shows that algorithmic governance reflects and amplifies systemic weaknesses in the transparency regime, including privatization, secrecy, private sector cooptation, and reactive disclosure. These deficiencies highlight the urgent need to reorient transparency and accountability law toward meaningful public engagement in ongoing oversight. This shift requires rethinking FOIA’s core commitment to …