Umang Bhatt|Javier Antorán|Yunfeng Zhang|Q. Vera Liao|Prasanna Sattigeri|Riccardo Fogliato|Gabrielle Gauthier Melançon|Ranganath Krishnan|Jason Stanley|Omesh Tickoo|Lama Nachman|Rumi Chunara|Madhulika Srikumar|Adrian Weller|Alice Xiang
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
University of Cambridge|University of Cambridge|IBM (United States)|IBM (United States)|IBM (United States)|Carnegie Mellon University|Intel (United States)|Intel (United States)|Intel (United States)|New York University|University of Cambridge|Sony Corporation (United States)
algorithmic transparency entails exposing system properties various stakeholders purposes include understanding improving contesting predictions research algorithmic transparency predominantly focused explainability explainability attempts provide reasons machine learning models behavior stakeholders however understanding models specific behavior alone might enough stakeholders gauge whether model wrong lacks sufficient knowledge solve task hand paper argue considering complementary form transparency estimating communicating uncertainty associated model predictions first discuss methods assessing uncertainty characterize uncertainty used mitigate model unfairness augment decisionmaking build trustworthy systems finally outline methods displaying uncertainty stakeholders recommend collect information required incorporating uncertainty existing ml pipelines work constitutes interdisciplinary review drawn literature spanning machine learning visualizationhci design decisionmaking fairness aim encourage researchers practitioners measure communicate use uncertainty form transparency