SB
Solon Barocas
Principal Researcher
Microsoft Research (adjunct, Cornell University)
Foundational work on 'disparate impact' in machine learning and co-author of the 'Fairness and Machine Learning' textbook.
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The Fair Lending Model: How the Longest-Running Algorithmic Fairness Programs Work in Practice
U.S. financial institutions subject to fair lending laws have been running algorithmic fairness programs for decades. Despite this long history, remarkably little is known about how these requirements operate in practice. In this paper, we offer the first empirical account of how financial institutions test for and mitigate algorithmic discrimination on the ground. In doing so, we shed light on how the regulatory design of fair lending law and regulation have shaped the policies, processes, and
Problem Formulation and Fairness
Formulating data science problems is an uncertain and difficult process. It requires various forms of discretionary work to translate high-level objectives or strategic goals into tractable problems, necessitating, among other things, the identification of appropriate target variables and proxies. While these choices are rarely self-evident, normative assessments of data science projects often take them for granted, even though different translations can raise profoundly different ethical concer