During a proof-of-concept, your team uses AutoML to classify loan applications. Compliance officers ask to see why the model rejects certain applicants. According to responsible AI principles, what is the main reason you should add explainability to this model?
To automatically encrypt the training data so it cannot be accessed by unauthorized users.
To reduce the computational cost of running inferences by simplifying the model architecture.
To identify which input factors influence each prediction, helping detect bias and build trust with regulators.
To guarantee that the model always meets a predefined accuracy threshold on unseen data.
Explainability techniques reveal how each feature contributes to a machine-learning model's output, making its decision-making process transparent. This transparency helps you uncover potential bias, demonstrate fairness, and build trust with regulators and business stakeholders. Techniques such as feature attributions do not encrypt data, reduce compute costs, or guarantee accuracy; they focus on clarifying why predictions are made, not on changing data security, performance, or accuracy characteristics.
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