Assessing Trustworthiness of AI Training Dataset using Subjective Logic -- A Use Case on Bias

TL;DR

This paper introduces a formal framework using Subjective Logic to evaluate the trustworthiness of AI training datasets, focusing on properties like bias with uncertainty quantification.

Contribution

It is the first to formalize dataset trustworthiness assessment at the dataset level using Subjective Logic, supporting uncertainty-aware evaluations of properties like bias.

Findings

Framework captures class imbalance effectively

Method remains interpretable and robust in federated settings

Experimental evaluation on traffic sign dataset demonstrates effectiveness

Abstract

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.