I-trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiers

• URL: http://arxiv.org/abs/2501.15617v1
• Date: Sun, 26 Jan 2025 17:54:43 GMT
• Title: I-trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiers
• Authors: Ritwik Vashistha, Arya Farahi,
• Abstract summary: This work formalizes I-trustworthy framework -- a novel framework for assessing the trustworthiness of probabilistics for inference tasks. To assess I-trustworthiness, we use the local calibration error (LCE) and develop a method of hypothesis-testing. We present a diagnostic tool designed to identify and measure biases in cases of miscalibration.
• Score: 0.18416014644193066
• License:
• Abstract: As probabilistic models continue to permeate various facets of our society and contribute to scientific advancements, it becomes a necessity to go beyond traditional metrics such as predictive accuracy and error rates and assess their trustworthiness. Grounded in the competence-based theory of trust, this work formalizes I-trustworthy framework -- a novel framework for assessing the trustworthiness of probabilistic classifiers for inference tasks by linking local calibration to trustworthiness. To assess I-trustworthiness, we use the local calibration error (LCE) and develop a method of hypothesis-testing. This method utilizes a kernel-based test statistic, Kernel Local Calibration Error (KLCE), to test local calibration of a probabilistic classifier. This study provides theoretical guarantees by offering convergence bounds for an unbiased estimator of KLCE. Additionally, we present a diagnostic tool designed to identify and measure biases in cases of miscalibration. The effectiveness of the proposed test statistic is demonstrated through its application to both simulated and real-world datasets. Finally, LCE of related recalibration methods is studied, and we provide evidence of insufficiency of existing methods to achieve I-trustworthiness.

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