The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability?

TL;DR

This paper offers a theoretical framework for understanding how different types of explainable AI influence human behavior and cognition, emphasizing the importance of tailored explanations for ethical and effective collaboration.

Contribution

It introduces a paradigm linking XAI explanations to behavior change techniques like nudges and boosts, providing a method to measure their influence.

Findings

Local and concept-based explanations align with nudges.

Global and counterfactual explanations relate to boosts.

Proposes a measurement method for XAI influence.

Abstract

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.