AI Bias: Consequences and Mitigation
hackerone.com | blog | #ai-security | #ai-safety | #machine-learning | #healthcare | #ethics | #ai-bias | #training-data
Summary
A HackerOne researcher invokes Galton's wisdom-of-the-crowd to argue that diverse training data is essential for unbiased AI, citing racial bias in Optum's patient-care algorithm as a concrete harm.
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--- id: 1239 title: "AI Bias: Consequences and Mitigation" source_domain: hackerone.com source_url: "https://www.hackerone.com/blog/ai-bias-consequences-and-mitigation" published_at: "2025-04-18 17:23" tags: ["ai-security", "ai-safety", "machine-learning", "healthcare", "ethics", "ai-bias", "training-data"] phying_url: "https://phying.com/item/1239" lang: en --- # AI Bias: Consequences and Mitigation ## Summary A HackerOne researcher invokes Galton's wisdom-of-the-crowd to argue that diverse training data is essential for unbiased AI, citing racial bias in Optum's patient-care algorithm as a concrete harm. ## Intelligence Metadata - **Sources:** hackerone.com - **Published:** 2025-04-18 17:23 - **Original:** https://www.hackerone.com/blog/ai-bias-consequences-and-mitigation - **Phying News:** https://phying.com/item/1239