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Found 2 papers, 1 papers with code
Learning under Imitative Strategic Behavior with Unforeseeable Outcomes
Machine learning systems have been widely used to make decisions about individuals who may best respond and behave strategically to receive favorable outcomes, e. g., they may genuinely improve the true labels or manipulate observable features directly to game the system without changing labels.
Counterfactually Fair Representation
It was shown in \cite{kusner2017counterfactual} that a sufficient condition for satisfying CF is to \textbf{not} use features that are descendants of sensitive attributes in the causal graph.
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