AI safety and governance

Begin with the people and communities who may be affected, the decision or service involved, and the ways harm could occur. Harms may result from misuse, design that has not considered bias or safety, or unintended effects. Ethical questions arise at different stages of a project, so the people doing the work need to cooperate and decide who can identify, address, and escalate concerns.

A risk statement should be specific enough to guide action: name a possible harm, who could experience it, and the conditions that might make it more likely. Consider fairness and non-discrimination alongside safety, privacy, and accountability. Bias is one concern to investigate; fairness is a broader question about the effects and treatment of people. Use the comparison page to explore that distinction.

• Data and AI Ethics Framework

  Explains fairness and recommends assessing data representativeness, bias, and the possible impacts of data use.

AI safety

Work to identify and reduce harms caused by AI systems.

AI ethics

Examining values and responsibilities in AI development and use.

AI governance

Rules, processes, and accountability for developing and using AI.

Model bias

Systematic tendencies or errors that can disadvantage people or distort results.

Ai Bias Vs Fairness

Risk assessment connects a proposed use to its setting, purpose, affected people, and possible consequences. Ask what the system is intended to do, where its outputs enter a decision or service, and what could happen if it performs poorly or is used in an unexpected way. Include relevant people in the assessment so that assumptions about the use and its effects can be questioned.

Assessment is a continuing activity: test before deployment, monitor during use, and revisit the analysis when the system, context, or evidence changes. Record risks in a way that helps people compare their impact and likelihood, decide which need a response, and make the reasoning traceable. An assessment can also identify a viable non-AI approach or show that the intended use should not proceed.

• AI RMF Core

  Explains the Govern, Map, Measure, and Manage functions and the role of ongoing risk management.

A risk review becomes useful when each priority risk has an owner and a concrete response. Assign people to maintain the system, review its effects, and decide what to do when monitoring finds a problem. Document the roles, escalation route, decision process, and any residual risk that remains after controls are applied.

Controls should fit the use and its risks. Possible responses include testing, monitoring, review by people with authority to intervene, and a route for affected people to question or correct a decision. Keep records that let the organization trace what was assessed and what action followed. Revisit controls across the system’s lifecycle, and consider whether a non-AI approach could reduce potential impact.

• Data and AI Ethics Framework

  Provides public-sector guidance on governance, oversight, responsibilities, and routes to challenge decisions.

• AI RMF Core

  Defines human-AI roles and oversight, and describes documenting human oversight processes.