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.
| Original language | English |
|---|---|
| Title of host publication | CHI '23: CHI Conference on Human Factors in Computing Systems, Hamburg Germany, April 23 - 28, 2023 |
| Publisher | ACM |
| ISBN (Print) | 9781450394222 |
| DOIs | |
| Publication status | Published - 19 Apr 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 16 Peace, Justice and Strong Institutions
Keywords
- AI
- Artificial intelligence
- Pan natural language system
- System transparency
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