AI-washing: The Asymmetric Effects of Its Two Types on Consumer Moral Judgments
This study addresses ethical transparency issues for policymakers, marketers, and researchers in AI, though it is incremental as it builds on existing concepts of deceptive communication.
The paper tackled the problem of AI-washing, defined as overstating or understating a company's AI usage, and found through an experiment with 401 participants that deceptive denial leads to more negative consumer moral judgments than honest negation, while deceptive boasting has no significant effects.
As AI hype continues to grow, organizations face pressure to broadcast or downplay purported AI initiatives - even when contrary to truth. This paper introduces AI-washing as overstating (deceptive boasting) or understating (deceptive denial) a company's real AI usage. A 2x2 experiment (N = 401) examines how these false claims affect consumer attitudes and purchase intentions. Results reveal a pronounced asymmetry: deceptive denial evokes more negative moral judgments than honest negation, while deceptive boasting has no effects. We show that perceived betrayal mediates these outcomes. By clarifying how AI-washing erodes trust, the study highlights clear ethical implications for policymakers, marketers, and researchers striving for transparency.