Introduction

As AI systems take on consequential roles—screening applications, assisting clinicians, moderating content, and operating in the physical world—their failures carry real costs. AI safety is the practice of making these systems reliable, fair, and aligned with human intent, both today and as capabilities grow. It spans technical methods, organizational practices, and public policy.

Safety is not a feature added at the end; it is a property earned through design, evaluation, and oversight. This section introduces the main concerns and the tools used to address them.

Robustness and Reliability

A safe model behaves predictably outside ideal conditions. Robustness concerns performance under noisy, shifted, or adversarial inputs, where small perturbations can cause large errors. Reliable systems are tested against distribution shift, monitored in production, and designed to fail gracefully—deferring to humans or abstaining when uncertain rather than producing confident mistakes.

Fairness and Bias

Models learn from data that reflects historical and social patterns, and can therefore reproduce or amplify bias. Fairness work involves measuring disparate performance across groups, understanding the sources of bias in data and objectives, and weighing trade-offs among competing notions of fairness. Because no single metric captures every concern, fairness requires context, stakeholder input, and ongoing scrutiny rather than a one-time check.

Interpretability and Alignment

Interpretability seeks to explain why a model produces a given output, supporting debugging, accountability, and trust. Alignment addresses a deeper question: ensuring that a system's objectives and behavior match what people actually want, especially as models become more capable and autonomous. Techniques such as learning from human feedback, red-teaming, and evaluation against specified behaviors aim to close the gap between what we optimize and what we intend.

Governance and Responsible Deployment

Technical methods work alongside governance. Documentation, risk assessment, access controls, and human oversight translate principles into practice, while emerging laws and standards set expectations for transparency and accountability. Responsible deployment matches the level of caution to the stakes—light-touch for low-risk uses, rigorous for high-stakes ones—and treats safety as a continuous commitment across a system's lifecycle.