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AI needs rules and rails: Why governance must move beyond policy
As AI adoption accelerates, organizations need more than high-level policy — they need practical operational guardrails. Governance must translate abstract principles into enforceable standards covering data use, model accountability, and risk management. Without structured frameworks embedded into workflows, AI systems risk drifting from business objectives, creating compliance gaps and unintended consequences. Effective governance requires collaboration across technical, legal, and business teams.
Report: AI reasoning gains fail to eliminate racial and gender bias in medical research
Matched: medical
Researchers at Flinders University found that advanced AI reasoning models still reproduce racial and gender stereotypes when generating fictional medical patient cases, despite improvements in reasoning capabilities. The findings raise concerns about deploying such models in healthcare settings, where biased outputs could reinforce existing disparities in medical research and patient care.