AI Safety

Extracted Attributes

• Field

  Interdisciplinary field

• Primary Goal

  Preventing harmful outcomes from artificial intelligence systems

• Core Concerns

  Accidents, misuse, unintended consequences, existential risks from advanced AI models, algorithmic bias, AI centralization, job displacement, AI copyright issues

• Key Components

  AI alignment, AI risk monitoring, AI robustness enhancement

• Related Concepts

  AI security (distinct but related), techno-optimism, techno-pessimism, techno-realism, AI regulation, open-source AI, fair use (copyright)

• Key Individuals/Groups

  Researchers, CEOs, Naval Ravikant, Sam Altman, Ilia Sutskever, Jen Easterly, Duncan O'Daniel Eddy

• Key Practices/Measures

  Algorithmic bias detection and mitigation, robustness testing and validation, Explainable AI (XAI), ethical AI frameworks, human oversight, security protocols, industry-wide collaboration, tamper-resistant safeguards for open-weight models, risk assessment efforts, whistleblowing policy, user privacy (default settings, opt-in consent, private hosting)

• Organizations/Entities Involved

  US AI Safety Institute, UK AI Safety Institute, IBM, Securiti, U.S. National Institute of Standards and Technology (NIST), Center for Security and Emerging Technology (CSET), Organisation for Economic Co-operation and Development (OECD), Cloud Security Alliance (CSA), Future of Life Institute (FLI)

Timeline

• AI safety gained significant momentum and popularity due to rapid progress in generative AI and increasing public concerns about potential dangers. (Source: Summary, Wikipedia)

  2023-01-01

• The US AI Safety Institute and UK AI Safety Institute were established during the 2023 AI Safety Summit. (Source: Summary, Wikipedia)

  2023-01-01

• Researchers express ongoing concern that AI safety measures are not keeping pace with the rapid development of AI capabilities. (Source: Summary, Wikipedia)

  2024-01-01

• Ongoing discussions around AI safety involve broader debates on techno-optimism versus techno-pessimism and the role of regulation, including advocacy for open-source AI. (Source: Summary, Related Documents)

  2024-01-01

• Ongoing concerns about AI extend to potential job displacement and complex economic and legal challenges, such as AI copyright and related lawsuits. (Source: Summary, Related Documents)

  2024-01-01

AI safety

AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses AI alignment (which aims to ensure AI systems behave as intended), monitoring AI systems for risks, and enhancing their robustness. The field is particularly concerned with existential risks posed by advanced AI models. Beyond technical research, AI safety involves developing norms and policies that promote safety. It gained significant popularity in 2023, with rapid progress in generative AI and public concerns voiced by researchers and CEOs about potential dangers. During the 2023 AI Safety Summit, the United States and the United Kingdom both established their own AI Safety Institute. However, researchers have expressed concern that AI safety measures are not keeping pace with the rapid development of AI capabilities.

Web Search Results

• The outlook for AI safety regulation in the US - IAPP

  AI safety broadly refers to the idea of practices and principles surrounding the development and use of AI in a way that prevents harmful outcomes to humans, according to entities including IBM, Securiti, the U.S. National Institute of Standards and Technology and the Center for Security and Emerging Technology. The Organisation for Economic Co-operation and Development, an intergovernmental group promoting sustainable growth and development, counts security and safety as part of its core [...] Part of the challenge around understanding AI safety is how universal the technology has become, according to Duncan O'Daniel Eddy, a Stanford University Center for AI Safety research fellow who was speaking on his own behalf. AI has a variety of use cases across sectors, which makes it more challenging to talk about safety without specifying what application of the technology is being addressed. [...] "AI systems should be robust, secure and safe throughout their entire lifecycle so that, in conditions of normal use, foreseeable use or misuse, or other adverse conditions, they function appropriately and do not pose unreasonable safety and/or security risks," the OECD principle reads.

• What Is AI Safety? - IBM

  AI safety and AI security are related but distinct aspects of artificial intelligence. AI safety aims to address inherent issues and unintended consequences, while AI security focuses on protecting AI systems from external threats. AI safety tries to connect AI with human values and reduce the chance that AI systems have a negative impact on businesses and society. It emphasizes AI alignment, which is the process of encoding human values and goals into AI models. [...] AI safety is a complex and evolving field that requires collaboration among researchers, industry leaders and policymakers. Many businesses participate in industry consortia, research initiatives and standardization efforts to share knowledge, best practices and lessons learned. By working together, the AI community can develop more robust and reliable safety measures. Who is responsible for AI safety? --------------------------------- [...] AI leaders and businesses are implementing various practices to support the responsible development and use of AI technologies. AI safety measures include: Algorithmic bias detection and mitigation Robustness testing and validation Explainable AI (XAI) Ethical AI frameworks Human oversight Security protocols Industry-wide collaboration ### Algorithmic bias detection and mitigation

• AI Safety vs. AI Security: Navigating the Commonality and Differences

  One thing we can say for sure is that AI safety encompasses a broad spectrum of concerns, surpassing traditional cybersecurity to encompass the alignment of AI systems with human values, system reliability, transparency, fairness, and privacy protection. Through proactive measures addressing these facets, AI safety aims to mitigate unintended harm or negative outcomes and advocate for the ethical development and deployment of AI systems. ### 4.1. Alignment with Human Values [...] This industry-led initiative aims to propel AI safety research forward, discern best practices for the responsible development and deployment of frontier models, and foster partnerships with policymakers and academia to disseminate insights on trust and safety risks. Moreover, the Forum endeavors to support endeavors leveraging AI to tackle pressing societal challenges such as climate change mitigation, early cancer detection, and cybersecurity.

• 2025 AI Safety Index - Future of Life Institute

  The Future of Life Institute's AI Safety Index provides an independent assessment of seven leading AI companies' efforts to manage both immediate harms and catastrophic risks from advanced AI systems. The Index aims to strengthen incentives for responsible AI development and to close the gap between safety commitments and real-world actions. The Summer 2025 version of the Index evaluates seven leading AI companies on an improved set of 33 indicators of responsible AI development and deployment [...] Significantly increase investment in technical safety research, especially tamper-resistant safeguards for open-weight models. Ramp up risk assessment efforts and publish implemented evaluations in upcoming model cards. Publish a full whistleblowing policy to match OpenAI’s transparency standard. Zhipu AI Publish the AI Safety Framework promised at the AI Summit in Seoul. Ramp up risk assessment efforts and publish implemented evaluations in upcoming model cards. DeepSeek [...] This indicator reports a company's dedication to user privacy when training and deploying AI models. It considers whether user inputs (such as chat history) are used by default to improve AI models or if companies require explicit opt-in consent. It also considers whether users can run powerful models privately, through on-premise deployment or secure cloud setups. Evidence includes default privacy settings and the availability of model weights for private hosting. Why This Matters

• AI Safety Initiative: Pioneering AI Compliance & Safety | CSA

  CSA’s AI Safety Initiative is the premier coalition of trusted experts who converge to develop and deliver essential AI guidance and tools that empower organizations of all sizes to deploy AI solutions that are safe, responsible, and compliant. [...] > AI will be the most transformative technology of our lifetimes, bringing with it both tremendous promise and significant peril. Through collaborative partnerships like this, we can collectively reduce the risk of these technologies being misused by taking the steps necessary to educate and instill best practices when managing the full lifecycle of AI capabilities, ensuring—most importantly—that they are designed, developed, and deployed to be safe and secure. Jen Easterly [...] > AI will be the most transformative technology of our lifetimes, bringing with it both tremendous promise and significant peril. Through collaborative partnerships like this, we can collectively reduce the risk of these technologies being misused by taking the steps necessary to educate and instill best practices when managing the full lifecycle of AI capabilities, ensuring—most importantly—that they are designed, developed, and deployed to be safe and secure. Jen Easterly