Racism, the criminal justice system, and data science

Racism, the criminal justice system, and data science

As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into one of the ways that data science perpetuates and amplifies racism in the American criminal justice system. COMPAS is an algorithm that claims to give a prediction about the likelihood of an offender to re-offend if released, based on the attributes of the individual, and guess what: it shows disparities in the predictions for black and white offenders that would nudge judges toward giving harsher sentences to black individuals. We dig into this algorithm a little more deeply, unpacking how different metrics give different pictures into the “fairness” of the predictions and what is causing its racially disparate output (to wit: race is explicitly not an input to the algorithm, and yet the algorithm gives outputs that correlate with race—what gives?) Unfortunately it’s not an open-and-shut case of a tuning parameter being off, or the wrong metric being used: instead the biases in the justice system itself are being captured in the algorithm outputs, in such a way that a self-fulfilling prophecy of harsher treatment for black defendants is all but guaranteed. Like many other things this week, this episode left us thinking about bigger, systemic issues, and why it’s proven so hard for years to fix what’s broken.

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Episoder(323)

Constitutional AI

Constitutional AI

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A Data-Driven Reality Check on AI in Business (Interview with Tom Davenport, Babson College)

A Data-Driven Reality Check on AI in Business (Interview with Tom Davenport, Babson College)

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Understanding AI Text Watermarking

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Anthropic just announced they're baking invisible watermarks directly into Claude's generated text — and while everyone else was busy having opinions about it, we were busy asking the more interesting...

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Better Know a Benchmark: Humanity's Last Exam

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17 Aug 23min

A Scientific Deep Dive into Overconfident LLMs: Interview with Kaitlyn Zhou (Cornell)

A Scientific Deep Dive into Overconfident LLMs: Interview with Kaitlyn Zhou (Cornell)

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10 Aug 33min

Reasoning Models: When LLMs Went Beyond Fancy Autocomplete

Reasoning Models: When LLMs Went Beyond Fancy Autocomplete

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3 Aug 25min

Distillation, or, How to Steal a Model

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27 Jul 23min

Invisible LLM Failures and AI Fluency with Chris Potts (Stanford)

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What happens when a Stanford linguistics professor turns his attention to AI chatbots — and the surprisingly invisible ways humans misunderstand them? Chris Potts joins the show to unpack the hidden f...

20 Jul 41min

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