Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set
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| Format: | Preprint |
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2025
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| _version_ | 1866915123158843392 |
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| author | Dai, Gordon Ravishankar, Pavan Yuan, Rachel Neill, Daniel B. Black, Emily |
| author_facet | Dai, Gordon Ravishankar, Pavan Yuan, Rachel Neill, Daniel B. Black, Emily |
| contents | When selecting a model from a set of equally performant models, how much unfairness can you really reduce? Is it important to be intentional about fairness when choosing among this set, or is arbitrarily choosing among the set of ''good'' models good enough? Recent work has highlighted that the phenomenon of model multiplicity-where multiple models with nearly identical predictive accuracy exist for the same task-has both positive and negative implications for fairness, from strengthening the enforcement of civil rights law in AI systems to showcasing arbitrariness in AI decision-making. Despite the enormous implications of model multiplicity, there is little work that explores the properties of sets of equally accurate models, or Rashomon sets, in general. In this paper, we present five main theoretical and methodological contributions which help us to understand the relatively unexplored properties of the Rashomon set, in particular with regards to fairness. Our contributions include methods for efficiently sampling models from this set and techniques for identifying the fairest models according to key fairness metrics such as statistical parity. We also derive the probability that an individual's prediction will be flipped within the Rashomon set, as well as expressions for the set's size and the distribution of error tolerance used across models. These results lead to policy-relevant takeaways, such as the importance of intentionally looking for fair models within the Rashomon set, and understanding which individuals or groups may be more susceptible to arbitrary decisions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_15634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set Dai, Gordon Ravishankar, Pavan Yuan, Rachel Neill, Daniel B. Black, Emily Computers and Society Machine Learning When selecting a model from a set of equally performant models, how much unfairness can you really reduce? Is it important to be intentional about fairness when choosing among this set, or is arbitrarily choosing among the set of ''good'' models good enough? Recent work has highlighted that the phenomenon of model multiplicity-where multiple models with nearly identical predictive accuracy exist for the same task-has both positive and negative implications for fairness, from strengthening the enforcement of civil rights law in AI systems to showcasing arbitrariness in AI decision-making. Despite the enormous implications of model multiplicity, there is little work that explores the properties of sets of equally accurate models, or Rashomon sets, in general. In this paper, we present five main theoretical and methodological contributions which help us to understand the relatively unexplored properties of the Rashomon set, in particular with regards to fairness. Our contributions include methods for efficiently sampling models from this set and techniques for identifying the fairest models according to key fairness metrics such as statistical parity. We also derive the probability that an individual's prediction will be flipped within the Rashomon set, as well as expressions for the set's size and the distribution of error tolerance used across models. These results lead to policy-relevant takeaways, such as the importance of intentionally looking for fair models within the Rashomon set, and understanding which individuals or groups may be more susceptible to arbitrary decisions. |
| title | Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2501.15634 |