This article aims to provide a theoretical account and corresponding paradigm for analysing how explainable artificial intelligence (XAI) influences people's behaviour and cognition. It uses insights from research on behaviour change. Two notable frameworks for thinking about behaviour change techniques are nudges - aimed at influencing behaviour - and boosts - aimed at fostering capability. It proposes that local and concept-based explanations are more adjacent to nudges, while global and counterfactual explanations are more adjacent to boosts. It outlines a method for measuring XAI influence and argues for the benefits of understanding it for optimal, safe and ethical human-AI collaboration.

翻译：本条旨在为分析可解释的人工智能(XAI)如何影响人们的行为和认知提供一个理论说明和相应的范例,它利用行为改变研究的深刻见解,关于行为改变技术的两个值得注意的思考框架是旨在影响行为(和推动)增强能力的手段,它建议基于概念的当地解释更接近手段,而全球解释和反事实解释则更接近动力,它概述了衡量XAI影响的方法,并论证了理解它的好处,以便实现最佳、安全和合乎道德的人类-AI合作。