BLIP: Facilitating the Exploration of Undesirable Consequences of Digital Technologies
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Abstract (arXiv)
Digital technologies have positively transformed society, but they have also led to undesirable consequences not anticipated at the time of design or development. We posit that insights into past undesirable consequences can help researchers and practitioners gain awareness and anticipate potential adverse effects. To test this assumption, we introduce BLIP, a system that extracts real-world undesirable consequences of technology from online articles, summarizes and categorizes them, and presents them in an interactive, web-based interface. In two user studies with 15 researchers in various computer science disciplines, we found that BLIP substantially increased the number and diversity of undesirable consequences they could list in comparison to relying on prior knowledge or searching online. Moreover, BLIP helped them identify undesirable consequences relevant to their ongoing projects, made them aware of undesirable consequences they "had never considered," and inspired them to reflect on their own experiences with technology.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 33 of its 122 references that are in the map. First posted on arXiv 10 May 2024.
Builds on
33 of 122 references.
References · 33
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21 more
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Built on by
Citing papers per year: 2024: 2; 2025: 3; 2026: 1.
6 papers.
- 2025Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Reviewcited by 16
- 2024Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviorscited by 5
- 2026Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgmentcited by 2
- 2025A Parameter-Efficient Mixture-of-Experts Framework for Cross-Modal Geo-Localizationcited by 1
- 2024Towards Leveraging News Media to Support Impact Assessment of AI Technologiescited by 0
- 2025PolicyPad: Collaborative Prototyping of LLM Policiescited by 0
- 2026Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment9 Feb 2026
- 2025A Parameter-Efficient Mixture-of-Experts Framework for Cross-Modal Geo-Localization23 Oct 2025
- 2025PolicyPad: Collaborative Prototyping of LLM Policies24 Sep 2025
- 2025Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review22 Jan 2025
- 2024Towards Leveraging News Media to Support Impact Assessment of AI Technologies4 Nov 2024
- 2024Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors26 Sep 2024