Promoting Ethical Awareness in Communication Analysis: Investigating Potentials and Limits of Visual Analytics for Intelligence Applications
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Abstract (arXiv)
Digital systems for analyzing human communication data have become prevalent in recent years. Intelligence analysis of communications data in investigative journalism, criminal intelligence, and law present particularly interesting cases, as they must take into account the often highly sensitive properties of the underlying operations and data. At the same time, these are areas where increasingly automated, sophisticated approaches systems can be particularly relevant, especially in terms of Big Data manageability. However, by the shifting of responsibilities, this also poses dangers. In addition to privacy concerns, these dangers relate to uncertain or poor data quality, leading to discrimination and potentially misleading insights. Visual analytics combines machine learning methods with interactive visual interfaces to enable human sense- and decision-making. This technique can be key for designing and operating meaningful interactive communication analysis systems that consider these ethical challenges. In this interdisciplinary work, a joint endeavor of computer scientists, ethicists, and scholars in Science & Technology Studies, we investigate and evaluate opportunities and risks involved in using Visual analytics approaches for communication analysis in intelligence applications in particular. We introduce, at first, the common technological systems used in communication analysis, further discussing the domain-specific ethical implications, tensions, and risks involved. We then make the case of how tailored Visual Analytics approaches may reduce and mitigate the described problems, both theoretically and through practical examples. We show that finding Visual Analytics design solutions for ethical issues is not a mere optimization task, but balancing out and negotiating these trade-offs has, as we argue, to be an integral aspect of the system design process from the outset.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Mar 2022.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2025Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Taskscited by 7
- 2024Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviewscited by 6
- 2026Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemakingcited by 4
- 2025Multimedia and Visual Analytics in the Agentic Eracited by 1
- 2025Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviewscited by 1
- 2026Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemaking16 Jan 2026
- 2025Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviews13 Oct 2025
- 2025Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks14 Apr 2025
- 2025Multimedia and Visual Analytics in the Agentic Era8 Apr 2025
- 2024Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews15 Jul 2024