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To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models
In fair machine learning, one source of performance disparities between groups is over-fitting to groups with relatively few training samples.
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation
Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist today.
Epistemic values in feature importance methods: Lessons from feminist epistemology
As the public seeks greater accountability and transparency from machine learning algorithms, the research literature on methods to explain algorithms and their outputs has rapidly expanded.
Feature Importance
Computers and Society
Problems with Shapley-value-based explanations as feature importance measures
Game-theoretic formulations of feature importance have become popular as a way to "explain" machine learning models.
