Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness
This work addresses the need for more reliable and truthful AI systems by providing a domain-specific benchmark and metric for multimodal uncertainty, though it is incremental as it builds on existing VQA frameworks.
The paper tackles the problem of uncertainty awareness in vision-language AI systems by introducing a taxonomy distinguishing epistemic and aleatoric uncertainty, and presents a benchmark dataset with 178K VQA samples and a new metric for evaluation.
The ability to acknowledge the inevitable uncertainty in their knowledge and reasoning is a prerequisite for AI systems to be truly truthful and reliable. In this paper, we present a taxonomy of uncertainty specific to vision-language AI systems, distinguishing between epistemic uncertainty (arising from a lack of information) and aleatoric uncertainty (due to inherent unpredictability), and further explore finer categories within. Based on this taxonomy, we synthesize a benchmark dataset, CertainlyUncertain, featuring 178K visual question answering (VQA) samples as contrastive pairs. This is achieved by 1) inpainting images to make previously answerable questions into unanswerable ones; and 2) using image captions to prompt large language models for both answerable and unanswerable questions. Additionally, we introduce a new metric confidence-weighted accuracy, that is well correlated with both accuracy and calibration error, to address the shortcomings of existing metrics.