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cs.LG2026
Is invariance all you need for algorithmic fairness? Removing demographic information can create new bias
Aditya Parikh, Eike Petersen, Stella Frank +3
Encoded demographic information in internal model representations is a commonly assumed risk factor for algorithmic bias, with demographic representation invariance often being tou…
cs.LG2026
Prevalence calibration as shortcut mitigation
Mohamed Amine Kina, Eike Petersen
Shortcut learning denotes the widespread situation in which a classifier exploits spurious correlations rather than diagnostic features. Existing mitigation strategies mostly aim t…