Algorithmic Harm

Algorithmic harm refers to negative outcomes that result from automated decision-making systems, including discrimination, privacy violations, economic damage, or physical injury caused by flawed or biased algorithms. Organizations must identify and mitigate these harms through impact assessments, testing, and monitoring to meet regulatory requirements and maintain stakeholder trust. This concept is central to AI governance because it encompasses the real-world consequences that compliance frameworks like the EU AI Act and Fair Lending regulations are designed to prevent.

1 item

EnforcementUS2026-09-25

Federal AI Prior Authorization Program Fails 53% of Requests, GAO Finds Procedural Breach

The Trump administration's WISeR pilot, launched in January 2026 across six states, uses AI to automate Medicare prior authorization decisions. One participating vendor denied more than 53 percent of requests, and several vendors missed a mandated 72-hour decision window. The Government Accountability Office determined in May 2026 that the Centers for Medicare and Medicaid Services did not follow proper procedure in establishing the program.

AI in healthcare prior authorization Centers for Medicare and Medicaid Services algorithmic harm human oversight government AI deployment GAO AI incident