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Full-Text Articles in Science and Technology Law
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 …
A Unified Theory Of Data, William Magnuson
A Unified Theory Of Data, William Magnuson
Faculty Scholarship
How does the proliferation of data in our modern economy affect our legal system? Scholars that have addressed the question have nearly universally agreed that the dramatic increases in the amount of data available to companies, as well as the new uses to which that data is being put, raise fundamental problems for our regulatory structures. But just what those problems might be remains an area of deep disagreement. Some argue that the problem with data is that current uses lead to discriminatory results that harm minority groups. Some argue that the problem with data is that it impinges on …
Beyond Transparency And Accountability: Three Additional Features Algorithm Designers Should Build Into Intelligent Platforms, Peter K. Yu
Faculty Scholarship
In the age of artificial intelligence, innovative businesses are eager to deploy intelligent platforms to detect and recognize patterns, predict customer choices and shape user preferences. Yet such deployment has brought along the widely documented problems of automated systems, including coding errors, corrupt data, algorithmic biases, accountability deficits and dehumanizing tendencies. In response to these problems, policymakers, commentators and consumer advocates have increasingly called on businesses seeking to ride the artificial intelligence wave to build transparency and accountability into algorithmic designs.
While acknowledging these calls for action and appreciating the benefits and urgency of building transparency and accountability into algorithmic …