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.

在可解释人工智能领域中，使用顺序反事实示例来改变经过训练的分类器的决策是常用的方法。本文针对现有方法的不足提出了使用分类器的输出概率来创建更具信息性的奖励以减轻潜在偏见等问题。