Algorithmic Bias
Systematic errors in AI outputs that create unfair outcomes for certain groups, typically arising from biased training data, flawed model design, or unrepresentative development teams.
Studies have documented significant bias in deployed AI systems: facial recognition error rates are up to 34x higher for dark-skinned women compared to light-skinned men, hiring algorithms have discriminated against women, and healthcare algorithms have underserved Black patients. Amazon scrapped an AI hiring tool in 2018 after it penalized resumes containing the word 'women's.' The EU AI Act classifies AI hiring and credit scoring tools as high-risk, requiring bias testing. Addressing algorithmic bias requires diverse training data, regular auditing, and inclusive development teams.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
AI Safety
The interdisciplinary field focused on preventing AI systems from causing harm, encompassing alignment, robustness, interpretability, and governance of AI technologies.
Deepfake
AI-generated synthetic media — images, video, or audio — that realistically depict events or statements that never occurred, created using deep learning techniques.
EU AI Act
The European Union's comprehensive AI regulation, which entered into force on August 1, 2024, classifying AI systems by risk level and imposing requirements from transparency disclosures to outright bans.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information, presenting it with the same confidence as accurate responses.
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