The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability?
This work addresses the need for optimal, safe, and ethical human-AI collaboration by providing a framework to analyze XAI's impact, though it is incremental as it builds on existing behavior change theories.
The paper tackles the problem of how explainable AI (XAI) influences human behavior and cognition by proposing a theoretical paradigm that categorizes explanations as nudges (influencing behavior) or boosts (fostering capability), with local/concept-based explanations linked to nudges and global/counterfactual ones to boosts.
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.