Could Counterfactuals Explain Algorithmic Decisions Without Opening the Black Box?

Algorithmic systems (such as those deciding mortgage applications, or sentencing decisions) can be very difficult to understand, for experts as well as the general public. The EU General Data Protection Regulation (GDPR) has sparked much discussion about the "right to explanation" for the algorithm-supported decisions made about us in our everyday lives. While there's an obvious need for transparency in the automated decisions that are increasingly being made in areas like policing, education, healthcare and recruitment, explaining how these complex algorithmic decision-making systems arrive at any particular decision is a technically challenging problem--to put it mildly. In their article "Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR" which is forthcoming in the Harvard Journal of Law & Technology, Sandra Wachter, Brent Mittelstadt, and Chris Russell present the concept of "unconditional counterfactual explanations" as a novel type of explanation of automated decisions that could address many of these challenges. Counterfactual explanations describe the minimum conditions that would have led to an alternative decision (e.g. a bank loan being approved), without the need to describe the full logic of the algorithm.