The Use of AI In Legal Case Analysis and Court Outcome Prediction: Opportunities, Challenges and Ethical Implications
Keywords:
Artificial Intelligence (AI); Legal Case Analysis; Court Outcome Prediction; Legal Ethics; Algorithm Bias; Transparency.Abstract
This study investigates Artificial Intelligence (AI) applications in legal case analysis and court outcome prediction within Indonesia's judicial system. Using qualitative descriptive analysis through comprehensive literature review of journal articles, books, and legal documents, this research examines opportunities, challenges, and ethical implications of AI implementation in legal practice. Findings reveal AI's significant potential for enhancing judicial efficiency through task automation, improving accuracy via pattern recognition and data analysis, and increasing accessibility through digital legal services. However, critical challenges include algorithmic bias perpetuating systemic inequalities, transparency deficits in decision-making processes, accountability gaps in AI recommendations, and data protection concerns under Indonesia's Personal Data Protection Law. Ethical implications encompass fairness issues in justice delivery, potential reduction of human oversight, privacy risks from data collection, and social impacts on legal profession dynamics. This research provides original insights through comprehensive analysis tailored to Indonesian legal framework, integrating Justice Theory, Computational Ethics, Legal Subject Theory, and Regulatory Theory. The study concludes that responsible AI integration requires developing regulatory frameworks, enhancing transparency mechanisms, addressing algorithmic bias, protecting personal data, maintaining human oversight, and promoting stakeholder collaboration to ensure ethical AI deployment in Indonesia's judicial system.
References
Alaslan, A. (2024). Qualitative research methods. https://doi.org/10.31237/osf.io/smrbh
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016, May 23). Machine bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. fairmlbook.org
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of Machine Learning Research, 81, 149-159.
Citron, D. K. (2008). Technological due process. Washington University Law Review, 85(6), 1249-1313.
Coglianese, C., & Lehr, D. (2017). Regulating by robot: Administrative decision making in the machine-learning era. Georgetown Law Journal, 105(5), 1147-1223.
Jones, A. (2021). AI and criminal justice: A comparative analysis. Law and Technology Review, 18(2), 120-145.
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018). Human decisions and machine predictions. Quarterly Journal of Economics, 133(1), 237-293.
Kroll, J., Huey, J., Barocas, S., Felten, E., Reidenberg, J., Robinson, D., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633-705.
Lee, S., & Kim, H. (2022). Patent litigation and artificial intelligence: A new frontier. Intellectual Property Law Quarterly, 25(4), 280-305.
Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
Ravizki, E. N., & Yudhantaka, L. (2022). Artificial intelligence as a legal subject: A conceptual review and regulatory challenges in Indonesia. Notaire, 5(3). https://doi.org/10.20473/ntr.v5i3.39063
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59-68.
Smith, J. (2020). Predicting legal outcomes with machine learning. Journal of Legal Studies, 45(3), 75-90.
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the general data protection regulation. International Data Privacy Law, 7(2), 76-99.
Wijaya, T. (2023). The development of innovative credit scoring systems in Indonesia: Assessing risks and policy challenges. https://doi.org/10.35497/560781
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Didy Hermawan, Burham Pranawa (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.