AI bias
Machine learning prejudice
AI bias refers to systematic errors in artificial intelligence systems that produce unfair outcomes—favoring or disfavoring particular groups based on race, gender, age, or other characteristics.
Bias can enter through training data that reflects historical discrimination, through choices made by developers, or through how results are interpreted.
The facial recognition software showed clear AI bias, failing to identify darker-skinned faces accurately.
The term has become central to debates about algorithmic fairness in hiring, lending, criminal justice, and content moderation.
- algorithmicFollowing programmed steps
- black boxOpaque system or device
- prejudicePreformed unfavorable opinion
- discriminationUnjust differential treatment
- fairnessJust and equal treatment
training · data · skewed · machine learning · training data · facial recognition · hiring · credit scoring · stereotypes · systematic · disparity · ethics · human rights · accountability · Amazon · GPT · racist · sexist
- digital societysocial networks, internet, digital age, internet culture, digital natives
- Digital Ethicsprivacy, data privacy, cyberbullying, misinformation, disinformation
- tech ethicsdata privacy, deepfakes, digital rights, tech addiction, privacy concerns
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