Summary of Long-term Fairness For Real-time Decision Making: a Constrained Online Optimization Approach, by Ruijie Du et al.
Long-term Fairness For Real-time Decision Making: A Constrained Online Optimization Approach
by Ruijie Du, Deepan Muthirayan, Pramod P. Khargonekar, Yanning Shen
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning has revolutionized many real-world systems, from predictive modeling to automation. However, it’s crucial to ensure machine learning-driven decision-making systems don’t violate ethical principles and societal values. As ML-driven decisions proliferate, particularly involving sensitive attributes like gender, race, and age, the need for equity and impartiality emerges as a fundamental concern. In real-time decision-making scenarios, fairness objectives become nuanced and complex: instantaneous fairness for equity in every time slot and long-term fairness over a period. Existing approaches mainly address dynamic costs with time-invariant fairness constraints, often disregarding time-varying fairness constraints. This work introduces a framework ensuring long-term fairness within dynamic decision-making systems characterized by time-varying fairness constraints. We formulate the problem as a constrained online optimization problem and present LoTFair, an online algorithm solving the problem on-the-fly. Proven to make overall fairness violations negligible while maintaining performance over the long run. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning has made many real-world systems smart! But we need to make sure these systems don’t treat people unfairly. For example, if a decision-making system is used in a hospital or school, it should be fair and not favor one group over another just because of their gender, race, or age. As these decisions are made quickly, fairness becomes even more important. Existing solutions mostly work when the rules for fairness don’t change, but what about when they do? This new research introduces a way to ensure long-term fairness in decision-making systems that need to be fair and fast. The approach uses an online algorithm called LoTFair, which makes sure decisions are fair while still working well over time. |
Keywords
* Artificial intelligence * Machine learning * Optimization
