Summary of A Human-in-the-loop Fairness-aware Model Selection Framework For Complex Fairness Objective Landscapes, by Jake Robertson et al.
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes
by Jake Robertson, Thorsten Schmidt, Frank Hutter, Noor Awad
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 educators can now summarize FairML applications with this medium-difficulty summary. This paper tackles the Impossibility Theorem of Fairness by framing fairness as a many-objective problem. ManyFairHPO is introduced, a human-in-the-loop framework that balances conflicting fairness metrics and their social consequences. Practitioners use it to make informed model-selection decisions. It effectively mitigates risks like self-fulfilling prophecies through comprehensive evaluation and case studies on the Law School Admissions problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This FairML paper helps with complex social objectives and legal requirements by treating fairness metrics as conflicting objectives. The ManyFairHPO framework aids in evaluating and balancing these conflicts, leading to better model-selection decisions. It’s useful for making socially responsible choices that take into account multiple fairness notions. |
Keywords
* Artificial intelligence * Machine learning
