Paper Number
ECIS2026-2058
Paper Type
CRP
Abstract
Courts face increasing pressure to handle growing caseloads while upholding fair and timely trials. This paper presents a systematic literature review of artificial intelligence (AI) use in courts through the lens of responsibility using paradox theory and the socio-technical perspective. We conceptualise responsibility as consisting of four dimensions: privacy, fairness, transparency, and accountability. Following PRISMA, we analyse 177 publications and conceptualise courts as information-processing systems in which AI can intervene along a judicial information pipeline. The review shows rapid growth but little structure: most work focuses on decision support framed as clustering or prediction, while responsibility dimensions are addressed unevenly and often only superficially. We synthesise six paradoxes (e.g., efficiency vs. legitimacy, data availability vs. privacy, accountability vs. automation) that shape and challenge responsible AI adoption in courts. This paper provides an overview of current research on responsible AI in courts and derives future research directions for this emerging field.
Recommended Citation
Aleknonyte-Resch, Milda; Jens, Jost; Bukhsh, Faiza; Brüning, Janique; and Lepsien, Arvid, "A Systematic Literature Review On Responsible AI In Judicial Processes: An Information Systems Perspective" (2026). ECIS 2026 Proceedings. 12.
https://aisel.aisnet.org/ecis2026/litrev/litrev/12
A Systematic Literature Review On Responsible AI In Judicial Processes: An Information Systems Perspective
Courts face increasing pressure to handle growing caseloads while upholding fair and timely trials. This paper presents a systematic literature review of artificial intelligence (AI) use in courts through the lens of responsibility using paradox theory and the socio-technical perspective. We conceptualise responsibility as consisting of four dimensions: privacy, fairness, transparency, and accountability. Following PRISMA, we analyse 177 publications and conceptualise courts as information-processing systems in which AI can intervene along a judicial information pipeline. The review shows rapid growth but little structure: most work focuses on decision support framed as clustering or prediction, while responsibility dimensions are addressed unevenly and often only superficially. We synthesise six paradoxes (e.g., efficiency vs. legitimacy, data availability vs. privacy, accountability vs. automation) that shape and challenge responsible AI adoption in courts. This paper provides an overview of current research on responsible AI in courts and derives future research directions for this emerging field.
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