Concrete Problems in AI Safety
OpenAI, Google Brain, Berkeley, and Stanford researchers co-authored 'Concrete Problems in AI Safety,' a foundational paper exploring research challenges in ensuring modern ML systems operate as intended. The paper identifies and frames specific technical safety problems for the field. Published in June 2016, it became a landmark reference for AI safety research agendas.
Related guides (3)
Related events (8)
AI Safety Needs Social Scientists
OpenAI published a paper arguing that long-term AI safety research requires social scientists to address uncertainties in human psychology, rationality, emotion, and biases that affect alignment algorithms. The paper contends that aligning advanced AI with human values cannot be solved by machine learning alone. OpenAI announced plans to hire social scientists full-time to work on these problems.
Our approach to AI safety
OpenAI published a high-level overview of its approach to AI safety, framing safe development and deployment as central to its mission. The post appears to be a brief, top-level statement rather than a detailed technical or policy document. It signals OpenAI's public positioning on safety at a time of growing regulatory and public scrutiny.
Anthropic publishes foundational 'Core Views on AI Safety' position paper
Anthropic released a detailed position paper outlining their core views on AI safety, arguing that transformative AI could arrive within a decade driven by predictable scaling laws, and that no one currently knows how to train powerful AI systems to robustly behave well. The document explains Anthropic's founding rationale and research strategy, highlighting four priority areas: scaling supervision, mechanistic interpretability, process-oriented learning, and understanding AI generalization. Originally published March 2023, this represents Anthropic's canonical public statement of their safety philosophy and strategic priorities.
Preparing for malicious uses of AI
OpenAI co-authored a multi-institutional paper forecasting how malicious actors could misuse AI technology, produced in collaboration with FHI, CSER, CNAS, EFF, and others over nearly a year. The paper outlines potential threat vectors and proposes prevention and mitigation strategies. This represents an early coordinated effort among AI safety and policy organizations to systematically address AI misuse risks.
OpenAI Safety Practices Update
OpenAI published a safety update reaffirming its commitment to responsible development and deployment of AGI. The post is a high-level statement from a Tier 1 lab on its safety posture. The body excerpt is brief and does not detail specific new policies, evaluations, or technical measures.
OpenAI Policy Paper: Four Strategies for Industry Cooperation on AI Safety
OpenAI published a policy research paper identifying four strategies to foster long-term industry cooperation on AI safety norms: communicating risks and benefits, technical collaboration, increased transparency, and incentivizing standards. The paper argues that competitive pressures risk creating a collective action problem where AI companies under-invest in safety. The analysis frames industry-wide coordination as essential to ensuring AI systems are safe and beneficial.
OpenAI and Anthropic Share Findings from Joint Safety Evaluation
OpenAI and Anthropic conducted a first-of-its-kind cross-lab safety evaluation, testing each other's frontier models across dimensions including misalignment, instruction following, hallucinations, and jailbreaking resistance. The collaboration represents a novel form of inter-lab safety research cooperation. Findings highlight both progress and ongoing challenges in AI safety, and establish a potential template for future cross-organizational evaluations.
AI Safety via Debate
OpenAI proposes a safety technique in which two AI agents debate a topic and a human judge determines the winner, with the goal of making it easier for humans to supervise AI systems that may be more capable than themselves. The core intuition is that it is easier to verify a correct argument than to generate one, so a dishonest agent can be caught by an honest opponent. The paper introduces debate as a scalable oversight mechanism applicable to complex tasks where direct human evaluation is infeasible.