Alleged Bias in IRS Algorithms: Impact on Black Taxpayers and the Need for Responsible AI
July 18, 2008
Reportedly, the design of IRS algorithms led to a higher audit rate among Black taxpayers. This underscores the importance of safe and secure AI, as these audits focused on easier-to-conduct reviews which coincidentally correlated with the group's pattern of tax filing errors. Ready to help prevent such incidents? JOIN US This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM).
- Alleged deployer
- internal-revenue-service
- Alleged developer
- internal-revenue-service
- Alleged harmed parties
- black-taxpayers
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/461
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.