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Counterfactuals and counterfactual reasoning underpin numerous techniques forauditing and understanding artificial intelligence (AI) systems. Thetraditional paradigm for counterfactual reasoning in this literature is theinterventional counterfactual, where hypothetical interventions are imaginedand simulated. For this reason, the starting point for causal reasoning aboutlegal protections and demographic data in AI is an imagined intervention on alegally-protected characteristic, such as ethnicity, race, gender, disability,age, etc. We ask, for example, what would have happened had your race beendifferent? An inherent limitation of this paradigm is that some demographicinterventions -- like interventions on race -- may not translate into theformalisms of interventional counterfactuals. In this work, we explore a newparadigm based instead on the backtracking counterfactual, where rather thanimagine hypothetical interventions on legally-protected characteristics, weimagine alternate initial conditions while holding these characteristics fixed.We ask instead, what would explain a counterfactual outcome for you as youactually are or could be? This alternate framework allows us to address many ofthe same social concerns, but to do so while asking fundamentally differentquestions that do not rely on demographic interventions.

A New Paradigm for Counterfactual Reasoning in Fairness and Recourse