Glossary

Discrimination in AI

What is 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.

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Topics

fairness
bias
AI ethics

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Related terms

Bias

In AI, bias refers to a systematic distortion of results. It often stems from unbalanced or historically skewed training data, but also from model design or how a question is framed. A well-known example: an applicant screening tool that learns from past hiring decisions can perpetuate existing disadvantages. In statistics, bias also means the deviation of an estimate from the true value. Countermeasures include diverse data, regular audits and human decisions on personnel matters.

AI ethics
Fairness
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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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Explainability

Explainability (explainable AI, XAI) describes how well one can understand why an AI system reached a particular result. It covers methods that show which inputs influenced a decision most, as well as practical aids such as source references and logs. Explainability matters for trust, troubleshooting and regulation – for example when a bank has to justify why an application was declined.

XAI
Transparency
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Human-in-the-Loop

Human-in-the-loop describes workflows in which people step into an AI process at defined points: they review results, approve or correct them, or take over difficult cases. The principle reduces the risk of faulty output and keeps accountability with people, especially for decisions affecting individuals, money or legal matters. Example: an AI agent drafts replies to customer enquiries and a member of staff checks them before they are sent.

Approval
Oversight
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