The Concept of Justice in AI-Driven Legal Decision Making

The integration of Artificial Intelligence (AI) into legal decision-making processes has introduced significant advancements in efficiency and predictive capability. However, its implications for justice—particularly fairness, impartiality, transparency, and due process—remain critically debated. This study employs a Systematic Literature Review (SLR) methodology to examine how AI-driven legal decision-making aligns with classical and contemporary philosophical concepts of justice. Drawing on 48 peer-reviewed articles, policy documents, and case studies published between 2015 and 2024, the research identifies four core thematic issues: the persistence of algorithmic bias, the lack of transparency in AI systems, inconsistencies in global regulatory frameworks, and the misalignment of AI logic with moral reasoning. While AI offers promising tools for streamlining judicial processes, its application often risks reinforcing existing inequities and undermining legal principles such as corrective justice and procedural fairness. The study concludes with targeted recommendations for the development of transparent, accountable, and ethically governed AI systems that support—rather than supplant—human judicial discretion. This research contributes to the growing discourse on legal AI by highlighting the necessity of embedding justice-oriented values at the core of technological innovation in the legal sector. This research has several limitations: not based on empirical findings and no validations from experts both in AI and in legal theories. Future research should address these limitations.

Authors

Institutions

Publication Details

Journal
bit-Tech
Published
2025-08-10
DOI
https://doi.org/10.32877/bt.v8i1.2338
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Concept of Justice in AI-Driven Legal Decision Making

Rasji Rasji, Elfindah Princes, Michael Michael
bit-Tech
Ethics and Social Impacts of AI
article

The Concept of Justice in AI-Driven Legal Decision Making

Rasji Rasji, Elfindah Princes, Michael Michael
article en

Abstract

The integration of Artificial Intelligence (AI) into legal decision-making processes has introduced significant advancements in efficiency and predictive capability. However, its implications for justice—particularly fairness, impartiality, transparency, and due process—remain critically debated. This study employs a Systematic Literature Review (SLR) methodology to examine how AI-driven legal decision-making aligns with classical and contemporary philosophical concepts of justice. Drawing on 48 peer-reviewed articles, policy documents, and case studies published between 2015 and 2024, the research identifies four core thematic issues: the persistence of algorithmic bias, the lack of transparency in AI systems, inconsistencies in global regulatory frameworks, and the misalignment of AI logic with moral reasoning. While AI offers promising tools for streamlining judicial processes, its application often risks reinforcing existing inequities and undermining legal principles such as corrective justice and procedural fairness. The study concludes with targeted recommendations for the development of transparent, accountable, and ethically governed AI systems that support—rather than supplant—human judicial discretion. This research contributes to the growing discourse on legal AI by highlighting the necessity of embedding justice-oriented values at the core of technological innovation in the legal sector. This research has several limitations: not based on empirical findings and no validations from experts both in AI and in legal theories. Future research should address these limitations.

bit-TechVol. 8(1)
Tarumanagara University (ID)
Peace, Justice and strong institutions
Openalex Percentile: Top 80%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.