A bank uses an AI model to help evaluate loan applications.

The primary ethical concern in this scenario isTransparency, often referred to in the AI field as the "Black Box" problem. Transparency in AI means that the processes, logic, and data used by the system to reach a decision should be understandable and accessible to human stakeholders. When bank employees cannot explainwhya loan was denied, it violates the principle of "Explainability," which is a subset of transparency.

This lack of transparency is particularly problematic in high-stakes industries like finance, healthcare, and law. If a model is making biased or incorrect decisions, the lack of transparency makes it impossible to audit the system or correct the underlying error. Many modern regulations, such as the GDPR’s "Right to Explanation," require that individuals affected by automated decisions have a right to know the logic behind them. Effective prompt engineering can help address this by using techniques like "Chain of Thought," where the AI is instructed to "show its work" or explain its reasoning process step-by-step, thereby transforming a black-box interaction into a more transparent, "white-box" process.