Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
This work addresses the problem of insufficient transparency in AI for stakeholders by proposing uncertainty as a complementary approach, though it is incremental as it reviews and synthesizes existing interdisciplinary literature.
The paper argues that uncertainty estimation should complement explainability as a form of algorithmic transparency, discussing methods to assess and communicate uncertainty to stakeholders for improving fairness, decision-making, and trustworthiness in machine learning systems.
Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most research into algorithmic transparency has predominantly focused on explainability. Explainability attempts to provide reasons for a machine learning model's behavior to stakeholders. However, understanding a model's specific behavior alone might not be enough for stakeholders to gauge whether the model is wrong or lacks sufficient knowledge to solve the task at hand. In this paper, we argue for considering a complementary form of transparency by estimating and communicating the uncertainty associated with model predictions. First, we discuss methods for assessing uncertainty. Then, we characterize how uncertainty can be used to mitigate model unfairness, augment decision-making, and build trustworthy systems. Finally, we outline methods for displaying uncertainty to stakeholders and recommend how to collect information required for incorporating uncertainty into existing ML pipelines. This work constitutes an interdisciplinary review drawn from literature spanning machine learning, visualization/HCI, design, decision-making, and fairness. We aim to encourage researchers and practitioners to measure, communicate, and use uncertainty as a form of transparency.