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.

本文提出了一个理论框架和相应的范式，用于分析可解释人工智能如何影响人们的行为和认知，结合行为改变的研究洞察。它提出了衡量 AI 影响并理解其对最佳、安全和道德的人机协作的益处的方法，同时认为本地和基于概念的解释更接近于推动，而全局和反事实解释更接近于促进能力。

可解释人工智能的影响：推动行为还是增强能力？

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

Auto-bidding has become one of the main options for bidding in online
advertisements, in which advertisers only need to specify high-level objectives
and leave the complex task of bidding to auto-bidders. In this paper, we
propose a family of auctions with boosts to improve welfare in auto-bidding
environments with both return on ad spend constraints and budget constraints.
Our empirical results validate our theoretical findings and show that both the
welfare and revenue can be improved by selecting the weight of the boosts
properly.

本文针对在线广告中的自动竞价进行了研究，提出了一种拥有加速法的拍卖家族，以改善竞价市场中的福利和收入。实证研究结果表明，适当选择加速法的权重可以改善市场福利和收入。