Cui bono? Judicial decision-making in the era of AI: A qualitative study on the expectations of judges in Germany
DOI:
https://doi.org/10.14512/tatup.33.1.14Keywords:
artificial intelligence, algorithmic judges, user-centered studies, e-justice, expert interviewsAbstract
Despite substantial artificial intelligence (AI) research in various domains, limited attention has been given to its impact on the judiciary, and studies directly involving judges are rare. We address this gap by using 20 in-depth interviews to investigate German judges’ perspectives on AI. The exploratory study examines (1) the integration of AI in court proceedings by 2040, (2) the impact of increased use of AI on the role and independence of judges, and (3) whether AI decisions should supersede human judgments if they were superior to them. The findings reveal an expected trend toward further court digitalization and various AI use scenarios. Notably, opinions differ on the influence of AI on judicial independence and the precedence of machine decisions over human judgments. Overall, the judges surveyed hold diverse perspectives without a clear trend emerging, although a tendency toward a positive and less critical evaluation of AI in the judiciary is discernible.
References
Berk, Richard (2019): Machine learning risk assessments in criminal justice settings. Cham: Springer. https://doi.org/10.1007/978-3-030-02272-3 DOI: https://doi.org/10.1007/978-3-030-02272-3
Dressel, Julia; Farid, Hany (2018): The accuracy, fairness, and limits of predicting recidivism. In: Science Advances 4 (1), p. eaao5580. https://doi.org/10.1126/sciadv.aao5580 DOI: https://doi.org/10.1126/sciadv.aao5580
Dreyer, Stephan; Schmees, Johannes (2019): Künstliche Intelligenz als Richter? Wo keine Trainingsdaten, da kein Richter. Hindernisse, Risiken und Chancen der Automatisierung gerichtlicher Entscheidungen. In: Computer und Recht 35 (11), pp. 758–764. https://doi.org/10.9785/cr-2019-351120 DOI: https://doi.org/10.9785/cr-2019-351120
Eidenmüller, Horst; Wagner, Gerhard (2021): Law by algorithm. Tübingen: Mohr Siebeck. https://doi.org/10.1628/978-3-16-157509-9 DOI: https://doi.org/10.1628/978-3-16-157509-9
Franke, Thomas; Attig, Christiane; Wessel, Daniel (2019): A personal resource for technology interaction. Development and validation of the affinity for technology interaction (ATI) scale. In: International Journal of Human-Computer Interaction 35 (6), pp. 456–467. https://doi.org/10.1080/10447318.2018.1456150 DOI: https://doi.org/10.1080/10447318.2018.1456150
Ghazizadeh, Mahtab; Lee, John; Boyle, Linda (2012): Extending the technology acceptance model to assess automation. In: Cognition, Technology & Work 14 (1), pp. 39–49. https://doi.org/10.1007/s10111-011-0194-3 DOI: https://doi.org/10.1007/s10111-011-0194-3
Greco, Luís (2021): Roboter-Richter? Eine Kritik. In: Hans-Georg Dederer and Yu-Cheol Shin (eds.): Künstliche Intelligenz und juristische Herausforderungen. Tübingen: Mohr Siebeck, pp. 103–122.
Grgić-Hlača, Nina; Engel, Christoph; Gummadi, Krishna (2019): Human decision making with machine assistance. In: Proceedings of the ACM on Human-Computer Interaction 3 (CSCW), pp. 1–25. https://doi.org/10.1145/3359280 DOI: https://doi.org/10.1145/3359280
Hartung, Dirk; Brunnader, Florian; Veith, Christian; Plog, Philipp; Wolters, Tim (2022): The future of digital justice. Boston: Boston Consulting Group. Available online at https://web-assets.bcg.com/3a/4a/66275bf64d92b78b8fabeb3fe705/22-05-31-the-future-of-digital-justice-bls-bcg-web.pdf, last accessed on 04. 01. 2024.
IBM Deutschland (2022): Unter Digitalisierungsdruck. Die Justiz auf dem Weg ins digitale Zeitalter. New York, NY: IBM Corporation.
Kuckartz, Udo; Rädiker, Stefan (2019): Analyzing qualitative data with MAXQDA. Text, audio, and video. Cham: Springer. DOI: https://doi.org/10.1007/978-3-030-15671-8
Myers, Michael; Newman, Michael (2007): The qualitative interview in IS research. Examining the craft. In: Information and Organization 17 (1), pp. 2–26. https://doi.org/10.1016/j.infoandorg.2006.11.001 DOI: https://doi.org/10.1016/j.infoandorg.2006.11.001
Nink, David (2021): Justiz und Algorithmen. Über die Schwächen menschlicher Entscheidungsfindung und die Möglichkeiten neuer Technologien in der Rechtsprechung. Berlin: Duncker & Humblot. https://doi.org/10.3790/978-3-428-58106-1 DOI: https://doi.org/10.3790/978-3-428-58106-1
Rädiker, Stefan; Kuckartz, Udo (2020): Focused analysis of qualitative interviews with MAXQDA. Step by Step. Berlin: MAXQDA Press.
Sheridan, Thomas; Verplank, William; Brooks, Thomas (1978): Human/computer control of undersea teleoperators. In: Proceedings of NASA Ames Research Center 14th Annual Conference on Manual Control, pp. 343–357. Available online at https://ntrs.nasa.gov/api/citations/19790007441/downloads/19790007441.pdf, last accessed on 15. 01. 2024.
Shi, Jiahui (2022): Artificial intelligence, algorithms and sentencing in Chinese criminal justice. Problems and solutions. In: Criminal Law Forum 33 (2), pp. 121–148. https://doi.org/10.1007/s10609-022-09437-5 DOI: https://doi.org/10.1007/s10609-022-09437-5
Skitka, Linda; Mosier, Kathleen; Burdick, Mark (2000): Accountability and automation bias. In: International Journal of Human-Computer Studies 52 (4), pp. 701–717. https://doi.org/10.1006/ijhc.1999.0349 DOI: https://doi.org/10.1006/ijhc.1999.0349
Stevenson, Megan (2018): Assessing risk assessment in action. In: Minnesota Law Review 103, pp. 303–384. Available online at https://scholarship.law.umn.edu/mlr/58, last accessed on 04. 01. 2024.
UNESCO – United Nations Educational, Scientific and Cultural Organization (2023): AI and the rule of law. Capacity building for judicial systems. Available online at https://www.unesco.org/en/artificial-intelligence/rule-law/mooc-judges, last accessed on 04. 01. 2024.
Watson, Joe; Aglionby, Guy; March, Samuel (2023): Using machine learning to create a repository of judgments concerning a new practice area. A case study in animal protection law. In: Artificial Intelligence and Law 31 (2), pp. 293–324. https://doi.org/10.1007/s10506-022-09313-y DOI: https://doi.org/10.1007/s10506-022-09313-y
Yalcin, Gizem; Themeli, Erlis; Stamhuis, Evert; Philipsen, Stefan; Puntoni, Stefano (2023): Perceptions of justice by algorithms. In: Artificial intelligence and Law 31 (2), pp. 269–292. https://doi.org/10.1007/s10506-022-09312-z DOI: https://doi.org/10.1007/s10506-022-09312-z
Yu, Eileen (2022): China wants legal sector to be AI‑powered by 2025. In: ZDNET/innovation, 12. 12. 2022. Available online at https://www.zdnet.com/article/china-wants-legal-sector-to-be-ai-powered-by-2025/, last accessed on 04. 01. 2024.
Završnik, Aleš (2020): Criminal justice, artificial intelligence systems, and human rights. In: ERA Forum 20 (4), pp. 567–583. https://doi.org/10.1007/s12027-020-00602-0 DOI: https://doi.org/10.1007/s12027-020-00602-0
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Anna-Katharina Dhungel, Moreen Heine
This work is licensed under a Creative Commons Attribution 4.0 International License.