Explanations Considered Harmful: The Impact of Misleading Explanations on Accuracy in Hybrid Human-AI Decision Making

Explainable AI (XAI) has the potential to enhance decision-making in human-AI collaborations, yet existing research indicates that explanations can also lead to undue reliance on AI recommendations, a dilemma often referred to as the ‘white box paradox.’ This paradox illustrates how persuasive explanations for incorrect advice might foster inappropriate trust in AI systems. Our study extends beyond the traditional scope of the white box paradox by proposing a framework for examining explanation inadequacy. We specifically investigate how accurate AI advice, when paired with misleading explanations, affects decision-making in logic puzzle tasks. Our findings introduce the concept of the ‘XAI halo effect,’ where participants were influenced by the misleading explanations to the extent that they did not verify the correctness of the advice, despite its accuracy. This effect reveals a nuanced challenge in XAI, where even correct advice can lead to misjudgment if the accompanying explanations are not coherent and contextually relevant. The study highlights the critical need for explanations to be both accurate and relevant, especially in contexts where decision accuracy is paramount. This calls into question the use of explanations in situations where their potential to mislead outweighs their transparency or educational value.