Glossary

AI and Human Rights

What is AI and Human Rights?

The term describes how AI systems can affect human rights such as privacy, equal treatment, freedom of expression or fair proceedings, both positively and negatively. Risks arise, for instance, from discriminatory decisions, mass surveillance, biometric identification or a lack of means to challenge decisions. International frameworks such as the Council of Europe's Framework Convention on AI and the EU AI Act address these issues. Organisations respond with impact assessments, transparency and human oversight.

Topics

Ethics
Fundamental rights
Regulation

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Related terms

AI Ethics

AI ethics deals with the moral questions surrounding the development and use of AI: fairness and discrimination, transparency and explainability, data protection, responsibility for errors, safety and the effects on work and society. For organisations it becomes concrete in guidelines, risk assessments, human oversight and honest communication about AI's limitations. Example: before a CV screening tool is introduced, it is checked for whether it disadvantages particular groups.

Ethics
Responsibility
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Discrimination in AI

Discrimination in AI occurs when an AI system unjustifiably disadvantages people on the basis of characteristics such as gender, ethnic origin, age or disability. Causes are usually skewed training data, unsuitable proxy features such as postcodes, or a lack of testing; well-known cases come from recruitment and lending. Countermeasures include representative data, regular fairness testing, transparency and human oversight of decisions about people.

fairness
bias
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Data protection regulations

Data protection regulations are legal rules governing the handling of personal data. In the EU the General Data Protection Regulation (GDPR) is the key framework; among other things it requires a legal basis for every processing activity, data minimisation, transparency towards data subjects and data processing agreements with service providers. When using AI, organisations need to clarify which data ends up in inputs, knowledge sources and logs, and where it is processed.

GDPR
data protection
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AI Transparency

AI transparency means disclosing when AI is involved, which data and sources its results are based on, and how decisions are reached. This includes labelling AI content, traceable source references, logs and documented changes. Transparency builds trust, makes troubleshooting easier and is partly required by law, for example by the EU AI Act's transparency obligations, under which people should be able to tell that they are interacting with an AI.

Transparency
Trust
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