# Paradoxes in Fair Computer-Aided Decision Making

arXiv: https://arxiv.org/abs/1711.11066 · HTML: https://mlsanity.com/paper/1711.11066/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [On the Fairness of Machine-Assisted Human Decisions](https://mlsanity.com/paper/2110.15310/) (2021)
- [Rethinking Algorithmic Fairness for Human-AI Collaboration](https://mlsanity.com/paper/2310.03647/) (2023)

## Abstract (arXiv)

Computer-aided decision making--where a human decision-maker is aided by a computational classifier in making a decision--is becoming increasingly prevalent. For instance, judges in at least nine states make use of algorithmic tools meant to determine "recidivism risk scores" for criminal defendants in sentencing, parole, or bail decisions. A subject of much recent debate is whether such algorithmic tools are "fair" in the sense that they do not discriminate against certain groups (e.g., races) of people. Our main result shows that for "non-trivial" computer-aided decision making, either the classifier must be discriminatory, or a rational decision-maker using the output of the classifier is forced to be discriminatory. We further provide a complete characterization of situations where fair computer-aided decision making is possible.