Kimon Kieslich, PhD

Kimon Kieslich, PhD

Postdoctoral Researcher

Publications

You can also find my articles on my Google Scholar profile.

Books

Journal Articles

Informing AI Policy Assessment using Large-Scale Simulation of Interventions

Published in Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency, 2026

Recommended citation: Barnett, J., Kieslich, K., Helberger, N., & Diakopoulos, N. (2026). Informing AI Policy Assessment using Large-Scale Simulation of Interventions. Proceeding of the ACM conference on Fairness, Accountability, and Transparency (ACM FAccT’26). https://doi.org/10.1145/3715275.3732096

Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society

Published in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2025

Recommended citation: Barnett, J., Kieslich, K., Sinchai, J., & Diakopoulos, N. (2025). A Systematic Literature Review of the Use of Scenario Methods to Explore the Future of Computing Technologies. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES-25), 8(1), 316–329. https://doi.org/10.1609/aies.v8i1.36551
Download Paper

Informing AI Risk Assessment with News Media: Analyzing National and Political Variation in the Coverage of AI Risks

Published in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2025

Recommended citation: Allaham, M., Kieslich, K., & Diakopoulos, N. (2025). Informing AI Risk Assessment with News Media: Analyzing National and Political Variation in the Coverage of AI Risks. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES-25), 8(1), 90–103.https://doi.org/10.1609/aies.v8i1.36533
Download Paper

Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making

Published in Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 2025

Recommended citation: Barnett, J., Kieslich, K., Helberger, N., & Diakopoulos, N. (2025). Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making. Proceeding of the ACM conference on Fairness, Accountability, and Transparency (ACM FAccT’25). https://doi.org/10.1145/3715275.3732096
Download Paper

But what is the alternative?! - The impact of generative AI on academic knowledge production in times of science under pressure

Published in Internet Policy Review, 2025

Recommended citation: Kieslich, K., & Schmitt, J. B. (2025). But what is the alternative?! - The impact of generative AI on academic knowledge production in times of science under pressure. Internet Policy Review. https:/ policyreview.info/articles/news/what-alternative-impact-generative-ai-academic-knowledge-production-times-science
Download Paper

Designing algorithms against corruption: a conjoint study on communicative features to encourage intentions for collective action

Published in Journal of Information Technology & Politics, 2025

Recommended citation: Starke, C., Kieslich, K., Reichert, M., & Köbis, N. (2025). Designing Algorithms against Corruption: A Conjoint Study on Communicative Features to Encourage Intentions for Collective Action. Journal of Information Technology & Politics, 1-17. https://doi.org/10.1080/19331681.2025.2465326
Download Paper

Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation

Published in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2024

Recommended citation: Barnett, J., Kieslich, K., & Diakopoulos, N. (2024). Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES-24). https://doi.org/10.1609/aies.v7i1.31620
Download Paper

Regulating AI-Based Remote Biometric Identification. Investigating the Public Demand for Bans, Audits, and Public Database Registrations

Published in Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

Recommended citation: Kieslich, K., & Lünich, M. (2024). Regulating AI-Based Remote Biometric Identification. Investigating the Public Demand for Bans, Audits, and Public Database Registrations. Conference Proceeding of the ACM conference on Fairness, Accountability, and Transparency (ACM FAccT). https://doi.org/10.1145/3630106.3658548
Download Paper

My Future with My Chatbot: A Scenario-Driven, User-Centric Approach to Anticipating AI Impacts

Published in Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparen, 2024

Recommended citation: Kieslich, K., Helberger, N., & Diakopoulos, N. (2024). My Future with My Chatbot: A Scenario-Driven, User-Centric Approach to Anticipating AI Impacts. Conference Proceeding of the ACM conference on Fairness, Accountability, and Transparency (ACM FAccT). https://doi.org/10.1145/3630106.3659026
Download Paper

Serving the populist agenda. How German public media broadcasters inadvertently facilitate populist movements by evoking fear and anger

Published in SCM Studies in Communication and Media, 2020

Recommended citation: Kieslich, K., & Marcinkowski, F. (2020). Serving the populist agenda. How German public media broadcasters inadvertently facilitate populist movements by evoking fear and anger. SCM Studies in Communication and Media, 9 (4), 482-515. https://doi.org/10.5771/2192-4007-2020-4-482
Download Paper

Implications of AI (un-)fairness in higher education admissions: the effects of perceived AI (un-)fairness on exit, voice and organizational reputation

Published in Conference Proceeding of the ACM conference on Fairness, Accountability, and Transparency (ACM FAT*), 2020

Recommended citation: Marcinkowski, F., Kieslich, K., Starke, C., & Lünich, M. (2020). Implications of AI (Un-)Fairness in Higher Education Admissions: The Effects of Perceived AI (Un-)Fairness on Exit, Voice and Organizational Reputation. Conference Proceeding of the ACM conference on Fairness, Accountability, and Transparency (ACM FAT*). https://doi.org/10.1145/3351095.3372867
Download Paper

Conference Papers

Using Scenario-Writing for Identifying and Mitigating Impacts of Generative AI

Published in NeurIPS workshop Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI (EvalEval), 2024

Recommended citation: Kieslich, K., Diakopoulos, N., & Helberger, N. (2024). Using Scenario-Writing for Identifying and Mitigating Impacts of Generative AI. NeurIPS workshop Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI (EvalEval). https://doi.org/10.48550/ARXIV.2410.23704
Download Paper

Towards Leveraging News Media to Support Impact Assessment of AI Technologies

Published in NeurIPS workshop Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI (EvalEval), 2024

Recommended citation: Allaham, M., Kieslich, K., & Diakopoulos, N. (2024). Towards Leveraging News Media to Support Impact Assessment of AI Technologies. NeurIPS workshop Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI (EvalEval). https://doi.org/10.48550/arXiv.2411.02536
Download Paper

Research Reports

Environmental Sustainability of Artificial Intelligence. How does the public perceive the environmental footprint of artificial intelligence?

Published in Factsheet Series of the Meinungsmonitor Künstliche Intelligenz, 2022

Recommended citation: Akyürek, S., Kieslich, K., Dosenovic, P., Marcinkowski, F., & Laukötter, E. (2022). Environmental Sustainability of Artificial Intelligence. How does the public perceive the environmental footprint of artificial intelligence. Factsheet No. 6 of the Meinungsmonitor Künstliche Intelligenz.[Opinion Monitor Artificial Intelligence].
Download Paper

Algorithmic recommendation systems. What does the German public think about the use and design of algorithmic recommendation systems?

Published in Factsheet Series of the Meinungsmonitor Künstliche Intelligenz, 2021

Recommended citation: Kieslich, K., Došenović, P., & Marcinkowski, F. (2021). Algorithmic recommendation systems. What does the German public think about the use and design of algorithmic recommendation systems?. Factsheet No. 4 of the Meinungsmonitor Künstliche Intelligenz [Opinion Monitor Artificial Intelligence].
Download Paper

Artificial Intelligence in Journalism. How does the public perceive the impact of artificial intelligence on the future of journalism?

Published in Factsheet Series of the Meinungsmonitor Künstliche Intelligenz, 2021

Recommended citation: Kieslich, K., Došenović, P., Starke, C., Lünich, M., & Marcinkowski, F. (2021). Artificial Intelligence in Journalism. How does the public perceive the impact of artificial intelligence on the future of journalism. Factsheet No. 4 of the Meinungsmonitor Künstliche Intelligenz.[Opinion Monitor Artificial Intelligence]
Download Paper

Artificial Intelligence in the Workplace. How does the German population perceive the influence of artificial intelligence on the future of the workplace?

Published in Factsheet Series of the Meinungsmonitor Künstliche Intelligenz, 2020

Recommended citation: Došenović, P., Starke, C., Kieslich, K., Baleis, J., & Marcinkowksi, F. (2020). Artificial Intelligence in the Workplace. How does the German population perceive the influence of artificial intelligence on the future of the workplace? Factsheet No. 3 of the Meinungsmonitor Künstliche Intelligenz.[Opinion Monitor Artificial Intelligence].
Download Paper

Artificial Intelligence and Discrimination. How does the German public think about the discrimination potential of artificial intelligence

Published in Factsheet Series of the Meinungsmonitor Künstliche Intelligenz, 2020

Recommended citation: Kieslich, K., Starke, C., Dosenovic, P., Keller, B., & Marcinkowski, F. (2020). Artificial Intelligence and Discrimination. How does the German public think about the discrimination potential of artificial intelligence. Factsheet No. 2 of the Meinungsmonitor Künstliche Intelligenz.[Opinion Monitor Artificial Intelligence].
Download Paper