⚖️ Responsible AI, Human Rights and Safety

Designing tools, content and services so people with different disabilities can use them effectively, safely and with dignity.

Clear assignment of who must answer for an AI system’s effects, act when problems arise, and provide correction or remedy when duties are not met.

The unequal ability of people, organisations or countries to build, access, use or shape AI systems and their benefits.

AI ethics is the practice of checking whether AI systems are fair, safe, accountable and respectful of human rights before and during their use.

AI safety means designing, testing and using AI systems so they are less likely to cause harm, especially in situations where people could be affected.

Systematic unfair patterns in data, rules or model outputs can disadvantage some people or groups, especially when tools are used in important decisions.

Confidentiality means limiting access to information so only authorised people can see, use or share it for a legitimate purpose.

A person freely agrees to a specific use of them or their information after getting clear, relevant information and a real chance to say yes or no.

The process of reviewing and acting on user content against rules to reduce harm, illegality and abuse while protecting lawful expression and human rights.

Data security means protecting digital or paper information from unauthorised access, change, loss or theft throughout its use, storage and sharing.

The gap between people who can reliably use digital tools and services and those who cannot, because of access, cost, skills, language, design or safety barriers.

A guardrail is a rule, check or control that limits unsafe or unacceptable AI outputs or actions before people rely on them.

Human oversight is the processes that let people monitor AI use, review its outputs and effects, and intervene when needed.

Inclusion means designing and using AI so different people can access it, be represented in it, and avoid being unfairly left out or harmed.

Privacy means controlling how information about identifiable people is collected, used, shared and kept, so they are not exposed, tracked or harmed unfairly.

An approach to designing, using and governing AI so it is safe, fair, accountable and under appropriate human control.

Toxicity means abusive, hostile or degrading output, including insults, threats or dehumanising content aimed at people or groups.

Being open about where an AI system is used, what it does, what information it uses, and who remains responsible for its effects.

The tendency to trust a system’s output too much and miss errors that a careful human review might have caught.

Explainability means being able to show, in human-usable terms, why an AI system produced a particular output or pattern of outputs.

Designing and checking AI systems so they do not produce avoidable, unjust differences in how people or groups are treated or affected.

An ongoing process to identify rights harms linked to an organisation’s actions or AI systems, address them, and account for the response.

A structured review used before or during AI use to identify who could be affected, what harms or rights risks may arise, and what controls or human decisions are needed.

Much AI depends on people who collect, label, review, filter and test data and outputs, often in outsourced or hidden forms of work.

People with real authority must have enough information and practical ability to shape, limit, change or stop AI-supported actions, especially in high-risk situations.

A principle and legal obligation to prevent unjustified harmful treatment based on protected traits or close proxies for them.

A structured attempt to make an AI system fail, so people can find risks, harms and weak controls before real users are affected.

Interpretability is the extent to which people can understand how a model works or produces outputs, sometimes by inspecting its internal parts.