Which outcome is commonly associated with bias in AI hiring?

• A. Legal challenges and discrimination
• B. Improved efficiency with no risk
• C. Guaranteed fairness
• D. Elimination of all bias automatically

Answer: (A)

Bias in AI hiring can lead to legal challenges and discrimination. When a hiring model learns from biased data or uses proxies for protected characteristics, it can systematically favor or disfavor certain groups, creating disparate impact that runs afoul of equal employment opportunity laws. That exposure often results in lawsuits, regulatory scrutiny, settlements, and reputational damage, plus the need to overhaul or disable the biased system. While AI can improve efficiency, the presence of bias introduces real risk, and fairness cannot be guaranteed or automatically achieved—bias can persist despite best intentions, so ongoing governance, data auditing, bias testing for disparate impact, and human oversight are essential.