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ML Safety HomeEventsFundingNewsletterResourcesCourse SafeBenchAboutGuidelinesExample IdeasFAQJudgesSubmitContact SafeBench AboutGuidelinesExample IdeasFAQJudgesContactSubmitExpress InterestEvents ML SAFETY The ML research community focused on reducing risks from 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 Adversarial Robustness, Long-Tail Robustness MONITORING Anomaly Detection, Interpretable Uncertainty, Transparency, Trojans, Detecting Emergent Behavior ALIGNMENT Honesty, Power Aversion, Value Learning, Machine Ethics SYSTEMIC SAFETY ML for Improved Epistemics, ML for Improved Cyberdefense, Cooperative AI Learn more ML SAFETY PROJECTS We organize AI/ML safety resources and education for researchers and non-technical audiences. Seminar Series (Coming Soon) The Newsletter NeurIPS 2023 Social Competitions and Prizes ML Safety Course GET CONNECTED Stay in the loop and exchange thoughts and news related to ML safety. Join our slack or follow one of the accounts below. Follow ML Safety @ml_safety General Announcements Follow ML Safety Daily @topofmlsafety ML safety papers as they are released A PROJECT BY THE CENTER FOR AI SAFETY MLSafety NewsletterFundingResourcesCourse SafeBench Submit BenchmarkSafeBench OverviewExample IdeasGuidelinesFrequently Asked QuestionsContactTerms and Conditions Events Events OverviewNeurIPS 2024NeurIPS 2023MLSS YaleICML SocialIntro to MLS © 2024 Center for AI Safety Built by Osborn Design Works