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AI for human rights practitioners
Where the harm actually lands: in the data, in the threshold, and in who never gets to appeal.
Algorithmic bias
Systematic unfair patterns in data, rules or model outputs can disadvantage some people or groups, especially when tools are used in important decisions.
Fairness
Designing and checking AI systems so they do not produce avoidable, unjust differences in how people or groups are treated or affected.
Non-discrimination
A principle and legal obligation to prevent unjustified harmful treatment based on protected traits or close proxies for them.
Privacy
Privacy means controlling how information about identifiable people is collected, used, shared and kept, so they are not exposed, tracked or harmed unfairly.
Consent
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.
Facial recognition
A computer vision method that compares face images or representations to estimate whether they show the same person.
Explainability
Explainability means being able to show, in human-usable terms, why an AI system produced a particular output or pattern of outputs.
Human rights due diligence
An ongoing process to identify rights harms linked to an organisation’s actions or AI systems, address them, and account for the response.
Meaningful human control
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.
Annotation and data labelling
People or software tools add task-relevant tags, notes or boundaries to data for model training, testing, analysis or monitoring.