Summary of Does Calibration Mean What They Say It Means; Or, the Reference Class Problem Rises Again, by Lily Hu
Does calibration mean what they say it means; or, the reference class problem rises again
by Lily Hu
First submitted to arxiv on: 21 Dec 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 The paper critiques the prevailing notion that statistical calibration within groups ensures fair treatment by showing that calibration does not necessarily imply consistent score interpretation across individuals from different groups. It argues that group-calibrated scores do not guarantee the same meaning or evidential value for an individual’s score, as they may belong to multiple groups. The paper claims that this reference class fallacy is prevalent in algorithmic fairness research and proposes a reevaluation of the dominant methodology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper challenges a common assumption about fairness in machine learning. It shows that just because scores are calibrated within certain groups, it doesn’t mean they have the same meaning for everyone. The author argues that people belong to many groups, so calibration alone can’t ensure fair treatment. Instead of relying on group-calibration, we should rethink how we approach algorithmic fairness. |
Keywords
» Artificial intelligence » Machine learning
