The Reputational Impact of AI Incidents: Modeling Downstream Effects on Sales and Firm Valuation

Abstract

Artificial intelligence (AI) incidents—ranging from biased algorithmic decisions to data breaches and harmful autonomous behavior—can damage firm reputation, potentially reducing sales and market valuation. This paper develops metrics to quantify reputational damage from AI-related scandals and empirically estimates their impact on sales and stock returns. Using a mixed-methods approach, we (1) construct an AI‑Incident Reputation Index (AIRI) combining event severity, media salience, regulatory action, and social sentiment; (2) perform sentiment analysis on news and social media to capture reputation shifts; and (3) estimate short- and long-term financial effects using event study methods for stock returns and panel regressions for quarterly sales. We compile a dataset of 86 AI incidents across public firms from 2016–2024 spanning sectors (technology, finance, healthcare, retail). Results show that severe AI incidents produce statistically significant negative abnormal returns in the short term (cumulative abnormal returns of −3.8% in a [-1,+1] window for high-severity incidents) and persistent sales declines for consumer-facing firms (avg. quarterly sales drop of 2.6% over two quarters). The AIRI and sentiment measures improve explanatory power beyond binary incident indicators. We discuss managerial implications for incident response, disclosure, and reputation management, and provide an event-driven toolkit for researchers and practitioners.