Designing Effective Training Dataset Explanations: The Impact of Information Depth and Progressive Disclosure

Transparency in AI is crucial for fostering user trust and acceptance, yet achieving it through explanations presents significant design challenges, particularly regarding how much detail to provide. For example, in-depth explanations can convey accurate and comprehensive information, but they also risk overwhelming users. This paper considers this important design tradeoff in the context of training dataset explanations, which describe the data used to train AI systems and differ from most model-centric explanations in terms of what and how much information they communicate. Specifically, we investigate how information depth in training dataset explanations and the use of Progressive Disclosure impact users’ understanding of an AI system (assessed via their critiques of the system), their system assessments, and their cognitive load. Findings from a study with 32 participants show advantages to providing users with comprehensive information on training datasets. Detailed explanations not only enhanced perceived trust, fairness, and understanding, but were also preferred by participants despite the increased cognitive load. While Progressive Disclosure did not effectively mitigate cognitive load, it improved users’ perception of learning. These findings suggest that effective transparency does not come from minimizing detail, but from embracing it, as participants consistently valued clarity and completeness over brevity, even at the cost of higher cognitive load.