Transparency is a fundamental requirement for decision making systems when
these should be deployed in the real world. It is usually achieved by providing
explanations of the system's behavior. A prominent and intuitive type of
explanations are counterfactual explanations. Counterfactual explanations
explain a behavior to the user by proposing actions -- as changes to the input
-- that would cause a different (specified) behavior of the system. However,
such explanation methods can be unstable with respect to small changes to the
input -- i.e. even a small change in the input can lead to huge or arbitrary
changes in the output and of the explanation. This could be problematic for
counterfactual explanations, as two similar individuals might get very
different explanations. Even worse, if the recommended actions differ
considerably in their complexity, one would consider such unstable
(counterfactual) explanations as individually unfair.
In this work, we formally and empirically study the robustness of
counterfactual explanations in general, as well as under different models and
different kinds of perturbations. Furthermore, we propose that plausible
counterfactual explanations can be used instead of closest counterfactual
explanations to improve the robustness and consequently the individual fairness
of counterfactual explanations.

本研究旨在正式和实证研究了解反事实解释的鲁棒性，以及在不同模型和不同类型扰动下的鲁棒性，并提出合理的反事实解释来改进鲁棒性和个体平等。

评价反事实解释的鲁棒性

Evaluating Robustness of Counterfactual Explanations

This paper presents a method for testing the decision making systems of
autonomous vehicles. Our approach involves perturbing stochastic elements in
the vehicle's environment until the vehicle is involved in a collision. Instead
of applying direct Monte Carlo sampling to find collision scenarios, we
formulate the problem as a Markov decision process and use reinforcement
learning algorithms to find the most likely failure scenarios. This paper
presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL)
solutions that can scale to large environments. We show that DRL can find more
likely failure scenarios than MCTS with fewer calls to the simulator. A
simulation scenario involving a vehicle approaching a crosswalk is used to
validate the framework. Our proposed approach is very general and can be easily
applied to other scenarios given the appropriate models of the vehicle and the
environment.

本文提出了一种测试自动驾驶汽车决策制定系统的方法，该方法使用强化学习算法找到最可能发生故障的场景，通过模拟车辆接近人行横道的情况证明了该方法的通用性。