An organization deploys an AI system to screen job applications. A risk assessment identifies that the model assigns lower scores to resumes containing terms associated with women's colleges. Which risk category does this represent?
Select an answer to reveal the explanation.
Short Explanation
You're seeing algorithmic bias causing disparate impact because the model systematically disadvantages women's college terms, a protected class. That's a core AI fairness risk, not just a technical glitch.
Full Explanation
The correct risk category is algorithmic bias causing disparate impact because the model systematically disadvantages a protected class (women), producing disparate impact — a core AI fairness risk. Operational risk from model underfitting is wrong because underfitting refers to poor generalization, not discriminatory outputs. Cybersecurity risk from data exfiltration is wrong because data exfiltration is a confidentiality breach, not a fairness issue. Reputational risk from model explainability gaps is wrong because while reputational risk may follow, the root cause is algorithmic bias, not explainability gaps.