{"ID":2897876,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.04352","arxiv_id":"2507.04352","title":"AI-washing: The Asymmetric Effects of Its Two Types on Consumer Moral Judgments","abstract":"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.","short_abstract":"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 fals...","url_abs":"https://arxiv.org/abs/2507.04352","url_pdf":"https://arxiv.org/pdf/2507.04352v1","authors":"[\"Greg Nyilasy\",\"Harsha Gangadharbatla\"]","published":"2025-07-06T11:28:45Z","proceeding":"cs.CY","tasks":"[\"cs.CY\",\"cs.AI\",\"cs.HC\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}