An interdisciplinary research field focused on ensuring that AI systems are reliable, controllable, and beneficial — addressing both near-term risks from current systems and long-term risks from potentially transformative future AI.
In Depth
AI Safety encompasses research and practices aimed at making AI systems that behave as intended, even in unexpected situations — and that remain under meaningful human oversight as their capabilities grow. The field distinguishes between near-term safety (ensuring current AI systems are robust, reliable, and don't cause immediate harm) and long-term safety (ensuring that future, potentially transformative AI systems remain aligned with human values and don't pose existential risks).
Near-term AI safety concerns include: model robustness (AI systems that fail gracefully on out-of-distribution inputs rather than producing dangerous outputs); adversarial robustness (resistance to inputs deliberately crafted to fool the model); bias and fairness (avoiding discriminatory harm at scale); reliability (consistent behavior in high-stakes applications like medical devices or autonomous vehicles); and privacy (protecting sensitive data used in training). These are engineering challenges with tractable near-term solutions.
Long-term AI safety focuses on the alignment problem — ensuring that increasingly capable AI systems pursue goals aligned with humanity's best interests. Key research programs include interpretability (understanding what AI systems are 'thinking'), scalable oversight (supervising AI behavior when systems are smarter than human supervisors), debate (having AI systems argue against each other's conclusions to surface flaws), and formal verification (mathematically proving properties of AI behavior). Organizations at the frontier — Anthropic, DeepMind's safety team, the Machine Intelligence Research Institute — dedicate significant resources to these challenges.
AI Safety is not about preventing science fiction scenarios — it is about the engineering discipline and research necessary to ensure that systems with increasing autonomy and capability remain reliable, controllable, and genuinely beneficial.
Real-World Applications
Frequently Asked Questions
What is the difference between AI Safety and AI Ethics?
AI Ethics focuses on the moral and social dimensions of AI: fairness, bias, privacy, accountability, and societal impact. AI Safety focuses on the technical challenge of ensuring AI systems behave as intended and remain under human control — especially as capabilities increase. Ethics asks 'should we build this?'; Safety asks 'if we build this, how do we make sure it works correctly and stays under control?'
What are the biggest near-term AI safety concerns?
Near-term concerns include: model robustness (reliable behavior on unexpected inputs), adversarial attacks (inputs crafted to fool models), hallucinations in LLMs (confident but false outputs), bias and discrimination (unfair outcomes at scale), privacy leakage (models memorizing and exposing training data), and misuse (using AI for disinformation, cyberattacks, or manipulation). These affect systems deployed today.
What organizations lead AI Safety research?
Major organizations include: Anthropic (Constitutional AI, interpretability), Google DeepMind (alignment, robustness), OpenAI (safety systems, red-teaming), the Machine Intelligence Research Institute (MIRI, theoretical alignment), the Center for AI Safety (CAIS, coordination), ARC Evals (capability evaluations), and academic labs at UC Berkeley, MIT, and Oxford. Governments are increasingly funding AI safety through institutions like the UK AI Safety Institute.