Incident 167: An Examination of a Potential Bias in a Retail Recommendation AI
Recent findings from Incident #167 highlight a potential bias within the recommendation algorithm of a popular retail company. The system, designed to suggest items based on user preferences, showed a disproportionate emphasis on certain product categories for users of a specific demographic. This incident underscores the need for responsible AI governance and the importance of trustworthy AI models in ensuring fairness and impartiality.
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/167
Data source
Incident data is from the AI Incident Database (AIID).
When citing the database as a whole, please use:
McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.
Pre-print on arXiv · Database snapshots & citation guide
We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.