# Inherent Trade-Offs in the Fair Determination of Risk Scores

**Authors:** Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan  
**arXiv:** [1609.05807](https://arxiv.org/abs/1609.05807)  
**Date:** 2016-09-19  
**Summary page:** https://www.summarizepaper.com/en/paper/1609.05807v2/

## Key points

- Authors: Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan
- Topic: Algorithmic classification and fairness
- Three fundamental fairness conditions introduced by authors
- No single method can satisfy all three conditions simultaneously in most scenarios
- Approximate fulfillment of conditions requires data alignment with constrained special cases
- Inherent incompatibilities between key fairness concepts in algorithmic classification
- Study provides valuable framework for navigating complexities of fairness in decision-making processes
- Practical insights offered for balancing competing notions of fairness

## Summary

In their paper titled "Inherent Trade-Offs in the Fair Determination of Risk Scores," authors Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan delve into the complex discussions surrounding algorithmic classification and fairness. The public discourse on this topic has highlighted the conflicting interpretations of fairness when it comes to probabilistic classification for different demographic groups. The authors introduce three fundamental fairness conditions that are central to these debates. They rigorously demonstrate that no single method can simultaneously satisfy all three conditions unless in highly restricted scenarios. Even achieving an approximate fulfillment of these conditions requires data alignment with an approximation of one of the constrained special cases outlined in their theorem. These findings shed light on the inherent incompatibilities between key concepts of fairness within algorithmic classification and provide a valuable framework for navigating the complexities and nuances involved in striving for fairness in decision-making processes. This study's implications extend beyond theoretical considerations as it offers practical insights into effectively balancing competing notions of fairness.