  Confidence scores of automatic speech recognition (ASR) outputs are often
inadequately communicated, preventing its seamless integration into analytical
workflows. In this paper, we introduce ConFides, a visual analytic system
developed in collaboration with intelligence analysts to address this issue.
ConFides aims to aid exploration and post-AI-transcription editing by visually
representing the confidence associated with the transcription. We demonstrate
how our tool can assist intelligence analysts who use ASR outputs in their
analytical and exploratory tasks and how it can help mitigate misinterpretation
of crucial information. We also discuss opportunities for improving textual
data cleaning and model transparency for human-machine collaboration.
