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title
ML Safety
description
The emerging research community focused on reducing long-term risks from ML systems.
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author
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2025-12-18 23:02:42
raw text
ML Safety Home Events Funding Newsletter Resources Events Course ML Safety The ML research community focused on reducing risks from advanced AI systems. What is ML Safety? ML systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. As with other powerful technologies, the safety of ML systems should be a leading research priority. This involves ensuring systems can withstand hazards ( Robustness ), identifying hazards ( Monitoring ), reducing inherent ML system hazards ( Alignment ), and reducing systemic hazards ( Systemic Safety ). Example problems and subtopics in these categories are listed below: Robustness: Adversaries, Long Tails Monitoring: Anomalies, Interpretable Uncertainty, Transparency, Trojans, Emergent Behavior Alignment: Honesty, Power Aversion, Value Learning, Machine Ethics Systemic Safety: ML for Improved Epistemics, ML for Improved Cyberdefense, Cooperative AI Learn more Se...
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11 - assets-global.website-files.com, newsletter.mlsafety.org, course.mlsafety.org, mlsafety.webflow.com, join.slack.com
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