Single-Query Black-Box Calibration Auditing via Logit Bias

This paper presents a method for auditing machine learning model calibration using only a single query to a black-box model, leveraging logit bias manipulation to extract calibration information. The technique enables enterprise practitioners to verify whether model confidence scores accurately reflect true prediction probabilities without requiring access to model internals or multiple queries.

This paper presents a method for auditing machine learning model calibration using only a single query to a black-box model, leveraging logit bias manipulation to extract calibration information. The technique enables enterprise practitioners to verify whether model confidence scores accurately reflect true prediction probabilities without requiring access to model internals or multiple queries. This is particularly relevant for regulated industries where model reliability and transparency auditing are critical compliance requirements.

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