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With the increasing importance of safety requirements associated with the useof black box models, evaluation of selective answering capability of models hasbeen critical. Area under the curve (AUC) is used as a metric for this purpose.We find limitations in AUC; e.g., a model having higher AUC is not alwaysbetter in performing selective answering. We propose three alternate metricsthat fix the identified limitations. On experimenting with ten models, ourresults using the new metrics show that newer and larger pre-trained models donot necessarily show better performance in selective answering. We hope ourinsights will help develop better models tailored for safety-criticalapplications.

Investigating the Failure Modes of the AUC metric and Exploring Alternatives for Evaluating Systems in Safety Critical Applications