Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
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arXiv
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| Format: | Preprint |
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2025
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| author | Pfohl, Stephen R. Harris, Natalie Nagpal, Chirag Madras, David Mhasawade, Vishwali Salaudeen, Olawale Dieng, Awa Sequeira, Shannon Arciniegas, Santiago Sung, Lillian Ezeanochie, Nnamdi Cole-Lewis, Heather Heller, Katherine Koyejo, Sanmi D'Amour, Alexander |
| author_facet | Pfohl, Stephen R. Harris, Natalie Nagpal, Chirag Madras, David Mhasawade, Vishwali Salaudeen, Olawale Dieng, Awa Sequeira, Shannon Arciniegas, Santiago Sung, Lillian Ezeanochie, Nnamdi Cole-Lewis, Heather Heller, Katherine Koyejo, Sanmi D'Amour, Alexander |
| contents | Disaggregated evaluation across subgroups is critical for assessing the fairness of machine learning models, but its uncritical use can mislead practitioners. We show that equal performance across subgroups is an unreliable measure of fairness when data are representative of the relevant populations but reflective of real-world disparities. Furthermore, when data are not representative due to selection bias, both disaggregated evaluation and alternative approaches based on conditional independence testing may be invalid without explicit assumptions regarding the bias mechanism. We use causal graphical models to characterize fairness properties and metric stability across subgroups under different data generating processes. Our framework suggests complementing disaggregated evaluations with explicit causal assumptions and analysis to control for confounding and distribution shift, including conditional independence testing and weighted performance estimation. These findings have broad implications for how practitioners design and interpret model assessments given the ubiquity of disaggregated evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness Pfohl, Stephen R. Harris, Natalie Nagpal, Chirag Madras, David Mhasawade, Vishwali Salaudeen, Olawale Dieng, Awa Sequeira, Shannon Arciniegas, Santiago Sung, Lillian Ezeanochie, Nnamdi Cole-Lewis, Heather Heller, Katherine Koyejo, Sanmi D'Amour, Alexander Machine Learning Computers and Society Disaggregated evaluation across subgroups is critical for assessing the fairness of machine learning models, but its uncritical use can mislead practitioners. We show that equal performance across subgroups is an unreliable measure of fairness when data are representative of the relevant populations but reflective of real-world disparities. Furthermore, when data are not representative due to selection bias, both disaggregated evaluation and alternative approaches based on conditional independence testing may be invalid without explicit assumptions regarding the bias mechanism. We use causal graphical models to characterize fairness properties and metric stability across subgroups under different data generating processes. Our framework suggests complementing disaggregated evaluations with explicit causal assumptions and analysis to control for confounding and distribution shift, including conditional independence testing and weighted performance estimation. These findings have broad implications for how practitioners design and interpret model assessments given the ubiquity of disaggregated evaluation. |
| title | Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2506.04193 |