The Model Room

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Where this idea comes from

When “fair” means two different things

In 2016 the newsroom ProPublica studied COMPAS, a score used in some US courts to estimate whether a defendant would re-offend. It found Black defendants who did not re-offend were wrongly flagged high-risk about twice as often as white defendants. The company that made the score answered that it was fair in another sense: among people given the same score, re-offending rates were about the same for both groups.

One thing to hold: A short mathematical result. In 2016 and 2017, Jon Kleinberg, Sendhil Mullainathan and Manish Raghavan, and separately Alexandra Chouldechova, showed that when two groups have different base rates, a score cannot be calibrated for both groups and also have equal false-positive and false-negative rates for both, except in special cases.

Why it matters: Both sides were measuring something real. A fairness definition is a choice about which mistake matters most, which is why this lab asks you to pick a threshold and say what it costs.

What we don’t know: Which definition should a court use is not a maths question, and people still disagree. The studies also argue about the data itself: an arrest record is not the same as a crime.

Dates:

  1. 2016 ProPublica publishes its COMPAS study; the maker replies.
  2. 2017 Two papers prove the fairness definitions cannot all hold at once.

Sources: J. Angwin, J. Larson, S. Mattu and L. Kirchner, “Machine Bias”, ProPublica (23 May 2016) · J. Kleinberg, S. Mullainathan and M. Raghavan, “Inherent Trade-Offs in the Fair Determination of Risk Scores”, ITCS 2017 (arXiv 2016) · A. Chouldechova, “Fair prediction with disparate impact”, Big Data 5(2), 2017