Learning impartial policies for sequential counterfactual explanations
  using Deep Reinforcement Learning
E. Panagiotou E. Ntoutsi
In the field of explainable Artificial Intelligence (XAI), sequential
counterfactual (SCF) examples are often used to alter the decision of a trained
classifier by implementing a sequence of modifications to the input instance.
Although certain test-time algorithms aim to optimize for each new instance
individually, recently Reinforcement Learning (RL) methods have been proposed
that seek to learn policies for discovering SCFs, thereby enhancing
scalability. As is typical in RL, the formulation of the RL problem, including
the specification of state space, actions, and rewards, can often be ambiguous.
In this work, we identify shortcomings in existing methods that can result in
policies with undesired properties, such as a bias towards specific actions. We
propose to use the output probabilities of the classifier to create a more
informative reward, to mitigate this effect.