Delayed Impact of Fair Machine Learning
| Autor: | Esther Rolf, Max Simchowitz, Lydia T. Liu, Moritz Hardt, Sarah Dean |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Population Machine Learning (stat.ML) Conventional wisdom 0603 philosophy ethics and religion Machine learning computer.software_genre Machine Learning (cs.LG) Statistics - Machine Learning education education.field_of_study business.industry 05 social sciences 050301 education 06 humanities and the arts Variable (computer science) Range (mathematics) Harm 060301 applied ethics Artificial intelligence business 0503 education computer Temporal modeling |
| Zdroj: | IJCAI |
| Popis: | Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs. 37 pages, 6 figures |
| Databáze: | OpenAIRE |
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