Today we’re joined by Ben Green, PhD Candidate at Harvard, Affiliate at the Berkman Klein Center for Internet & Society at Harvard, Research Fellow at the AI Now Institute at NYU.
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Ben’s research is focused on the social and policy impacts of data science, with a focus on algorithmic fairness, municipal governments, and the criminal justice system. In our conversation, we discuss his paper ‘Good’ Isn’t Good Enough,’ which explores the 2 things he feels are missing from data science and machine learning projects, papers and research; A grounded definition of what “good” actually means, and the absence of a “theory of change.” We also talk through how he thinks about the unintended consequence associated with the application of technology to social good, and his theory for the relationship between technology and social impact.
Connect with Ben!
- Paper: “Good” Isn’t Good Enough
- Paper: The False Promise of Risk Assessments: Epistemic Reform and the Limits of Fairness
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“More On That Later” by Lee Rosevere licensed under CC By 4.0