  Artificial Intelligence (AI) systems are increasingly used in high-stakes
domains of our life, increasing the need to explain these decisions and to make
sure that they are aligned with how we want the decision to be made. The field
of Explainable AI (XAI) has emerged in response. However, it faces a
significant challenge known as the disagreement problem, where multiple
explanations are possible for the same AI decision or prediction. While the
existence of the disagreement problem is acknowledged, the potential
implications associated with this problem have not yet been widely studied.
First, we provide an overview of the different strategies explanation providers
could deploy to adapt the returned explanation to their benefit. We make a
distinction between strategies that attack the machine learning model or
underlying data to influence the explanations, and strategies that leverage the
explanation phase directly. Next, we analyse several objectives and concrete
scenarios the providers could have to engage in this behavior, and the
potential dangerous consequences this manipulative behavior could have on
society. We emphasize that it is crucial to investigate this issue now, before
these methods are widely implemented, and propose some mitigation strategies.
