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