Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society
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Abstract (arXiv)
Scenario building is an established method to anticipate the future of emerging technologies. Its primary goal is to use narratives to map future trajectories of technology development and sociotechnical adoption. Following this process, risks and benefits can be identified early on, and strategies can be developed that strive for desirable futures. In recent years, computer science has adopted this method and applied it to various technologies, including Artificial Intelligence (AI). Because computing technologies play such an important role in shaping modern societies, it is worth exploring how scenarios are being used as an anticipatory tool in the field -- and what possible traditional uses of scenarios are not yet covered but have the potential to enrich the field. We address this gap by conducting a systematic literature review on the use of scenario building methods in computer science over the last decade (n = 59). We guide the review along two main questions. First, we aim to uncover how scenarios are used in computing literature, focusing especially on the rationale for why scenarios are used. Second, in following the potential of scenario building to enhance inclusivity in research, we dive deeper into the participatory element of the existing scenario building literature in computer science.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 102 references that are in the map. First posted on arXiv 5 Jun 2025.
Builds on
13 of 102 references.
References · 13
- 2021Ethical and social risks of harm from Language Modelscited by 966
- 2022Social Simulacra: Creating Populated Prototypes for Social Computing Systemscited by 301
- 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 92
- 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?cited by 83
- 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
- 2024Human vs. Machine: Behavioral Differences Between Expert Humans and Language Models in Wargame Simulationscited by 21
- 2022Seamful XAI: Operationalizing Seamful Design in Explainable AIcited by 20
- 2024ExploreGen: Large Language Models for Envisioning the Uses and Risks of AI Technologiescited by 12
- 2023Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Disputecited by 9
- 2020Anticipatory Ethics and the Role of Uncertaintycited by 8
- 2022The Dark Side of Perceptual Manipulations in Virtual Realitycited by 6
- 2024Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulationcited by 5
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.