AI Bias
Systematic errors in AI outputs that reflect prejudices in training data or model design.
Definition
AI bias refers to systematic and unfair discrimination in AI outputs resulting from biased training data, flawed algorithms, or problematic model design. These biases can perpetuate and amplify existing societal prejudices in automated decisions.
Bias manifests in various forms including demographic disparities, cultural assumptions, and historical inequities encoded in data. As AI increasingly influences hiring, lending, content recommendation, and other consequential decisions, addressing bias becomes ethically imperative.
Why It Matters
AI bias can cause real harm to individuals and groups who receive unfair treatment from automated systems. Organizations deploying biased AI face legal liability, reputational damage, and erosion of user trust.
Understanding bias helps organizations implement responsible AI practices. Testing for bias, diversifying training data, and establishing oversight processes protect both users and organizations from bias-related harms.
Examples in Practice
A hiring platform discovers its AI resume screener penalizes candidates with names common in certain ethnic groups, traced to bias in historical hiring data used for training.
A content recommendation system amplifies conspiracy content because engagement data reflects human tendencies toward sensationalism, requiring manual intervention to promote quality.
An image generation tool produces stereotypical depictions of certain professions by gender, leading to updates in training data and prompt engineering safeguards.