The Impact of System Transparency on Analytical Reasoning
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Abstract
In this paper, we present the hypothesis that system transparency is critical for tasks that involve expert sensemaking. Artificial Intelligence (AI) systems can aid criminal intelligence analysts, however, they are typically opaque, obscuring the underlying processes that inform outputs, and this has implications for sensemaking. We report on an initial study with 10 intelligence analysts who performed a realistic investigation exercise using the Pan natural language system [10, 11], in which only half were provided with system transparency. Differences between conditions are analysed and the results demonstrate that transparency improved the ability of analysts to reason about the data and form hypotheses.Description
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Sam Hepenstal, Leishi Zhang, and B. L. William Wong. 2023. The Impact of System Transparency on Analytical Reasoning. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA '23). Association for Computing Machinery, New York, NY, USA, Article 274, 1–6. https://doi.org/10.1145/3544549.3585786
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