Assessing Trustworthiness of AI Training Dataset using Subjective Logic -- A Use Case on Bias
This addresses the critical need for evaluating dataset-level trustworthiness, such as fairness, for AI developers and practitioners, though it is incremental as it extends prior work on Subjective Logic to dataset-level properties.
The paper tackles the problem of assessing trustworthiness in AI training datasets, particularly for global properties like bias, by introducing a formal framework based on Subjective Logic that enables uncertainty-aware evaluations; results show it captures class imbalance and remains interpretable and robust in centralized and federated contexts.
As AI systems increasingly rely on training data, assessing dataset trustworthiness has become critical, particularly for properties like fairness or bias that emerge at the dataset level. Prior work has used Subjective Logic to assess trustworthiness of individual data, but not to evaluate trustworthiness properties that emerge only at the level of the dataset as a whole. This paper introduces the first formal framework for assessing the trustworthiness of AI training datasets, enabling uncertainty-aware evaluations of global properties such as bias. Built on Subjective Logic, our approach supports trust propositions and quantifies uncertainty in scenarios where evidence is incomplete, distributed, and/or conflicting. We instantiate this framework on the trustworthiness property of bias, and we experimentally evaluate it based on a traffic sign recognition dataset. The results demonstrate that our method captures class imbalance and remains interpretable and robust in both centralized and federated contexts.