Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem

Artificial Intelligence (AI) is used in more and more high-stakes domains of our life such as justice [Berk, 2012], healthcare [Callahan and Shah, 2017], and finance [Lessmann et al., 2015], increasing the need to explain these decisions and to make sure that they are aligned with how we want the decision to be made. However, the complexity of many AI systems makes them challenging to comprehend, posing a significant barrier to their implementation and oversight [Arrieta et al., 2020, Samek et al., 2019]. Legislative initiatives, including the EU General Data Protection Regulation (GDPR), have recognized the'right for explanation' for individuals affected by algorithmic-decision making, emphasizing the legal necessity of explainability [Goodman and Flaxman, 2017]. In response, the field of Explainable Artificial Intelligence (XAI) has emerged, aimed at developing methods for explaining the decision-making processes of AI models [Adadi and Berrada, 2018, Holzinger et al., 2022, Xu et al., 2019]. Nevertheless, the landscape of post-hoc explanations is diverse, and each method can yield a different explanation. Furthermore, even within a single explanation method, multiple explanations can be generated for the same instance or decision. This phenomenon, known as the disagreement problem, has been studied in literature [Brughmans et al.,