A cloud computing company uses machine learning software to screen the resumes of job seekers.

The machine learning model unintentionally discriminates against minority job seekers because it was trained on historical hiring data that contained biases.

Why Exposing Bias in Data?

Machine learning systems reflect the biases present in their training data.

If past hiring decisions favored certain groups, the AI will replicate and reinforce those biases.

The audit revealed systematic discrimination, indicating bias in the data, not the AI itself.

B. Acting fairly toward candidates – The AI is not fair if it disproportionately excludes minority job seekers.

C. Exhibiting intent to harm applicants – The AI does not have intent; bias is an unintended consequence of flawed data.

D. Scoring low humility measurement – "Humility" is not a standard AI metric; bias exposure is the actual issue.

Why Not the Other Options?Thus, the correct answer is A. Exposing bias that exists in the data, as the AI amplifies historical discrimination in hiring.

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.

U.S. Equal Employment Opportunity Commission (EEOC) Guidelines on AI Hiring.

Floridi, L. (2013). The Ethics of Information.

References in Ethics in Technology: