Risks of using black-box models

Black-box models make decisions that are difficult for humans to understand or explain. We only see their inputs and outputs, not the reasoning behind them. For example, an algorithm that screens job applicants might reject qualified candidates without clear reasons. This lack of transparency can weaken trust and accountability. Hidden biases may be learned from past data and quietly amplified, leading to discrimination that often goes unnoticed until it causes harm. Since employment decisions are highly regulated and must be fair and auditable, black-box systems complicate compliance and investigations. Therefore, transparency and human oversight are crucial to mitigate these risks.