Artificial Intelligence in the Administration of Justice: A Normative Framework for Assisting Lawyers, Assisting Judges and Automated Adjudication Under the Right to Fair Hearing in Nigeria
DOI:
https://doi.org/10.68854/fx62yn21Keywords:
artificial intelligence, administration of justice, fair hearing, judicial decision-making, explainability, automation bias, right to a human decision, NigeriaAbstract
Courts in many jurisdictions face growing backlogs, and artificial intelligence (AI) is increasingly presented as a remedy. In Nigeria, where the Constitution guarantees a fair hearing within a reasonable time but delay is endemic, the temptation to automate is strong. Yet adjudication draws its legitimacy from impartiality, reasoned decision-making and accountability, qualities that some uses of AI may erode. Existing scholarship has tended to debate whether "robot judges" are desirable, leaving the more common and less visible uses of AI in justice systems under-examined, and Nigeria has received little attention. This study addresses that gap. Its objectives were to distinguish the roles AI can play in the administration of justice, to evaluate each against the requirements of fair hearing and to derive a framework of safeguards for Nigeria. The study adopted a doctrinal and normative research design with a comparative element. It analysed primary legal sources, including the Nigerian Constitution, the African Charter on Human and Peoples' Rights, the Evidence Act, the European Union's Artificial Intelligence Act and leading foreign decisions on AI in courts, together with a purposive sample of 28 peer-reviewed journal articles published between 2023 and 2026, each verified against its Crossref publisher record and screened for retractions. The analysis distinguishes three roles: assisting the lawyer, assisting the judge and deciding in place of the judge, and tests each against fairness, explainability and the right to a human decision. Lawyer-facing assistance is broadly compatible with fair hearing if duties of verification are enforced, given evidence that legal AI tools hallucinate frequently. Judge-facing assistance is acceptable only with safeguards, because automation bias and institutional pressure can turn recommendations into de facto decisions while the judge's signature conceals the shift. Automated adjudication fails all three standards except possibly in narrow, consensual and low-value matters. The study recommends practice directions on AI for lawyers and judges, a statutory prohibition on solely automated judicial decisions, and investment in case management, transcription and digital publication of judgments before any adoption of decision-support tools.
References
Altobelli, T., McKenna, E., & Suh, I. (2024). Revolutionizing family courts: Catalysts for reform and the transformative role of technology. Family Court Review, 62(2), 321–342. https://doi.org/10.1111/fcre.12783 Álvarez, M., & Matute, H. (2026). Susceptibility to algorithmic recommendations in simulated jury decision making. Legal and Criminological Psychology. Advance online publication. https://doi.org/10.1111/lcrp.70068
Anastasopoulos, L. J. (2026). Artificial intelligence, bureaucratic discretion, and democratic administration. Public Administration Review. Advance online publication. https://doi.org/10.1111/puar.70201
Anozie, M. C., & Wingate, E. O. (2023). The constitutionality conundrum on virtual court sittings in the Covid-19 period in Nigeria. African Journal of International and Comparative Law, 31(3), 377–395. https://doi.org/10.3366/ajicl.2023.0454
Chen, Y. (2026). Artificial intelligence and adjudication: A new pathway to justice in China? Computer Law & Security Review, 60, Article 106260. https://doi.org/10.1016/j.clsr.2026.106260
Cheng, K. Y. (2026). Responsible artificial intelligence in courts: A four-test framework. AI & Innovation, Article e70015. https://doi.org/10.1002/aiv2.70015
Dahl, M., Magesh, V., Suzgun, M., & Ho, D. E. (2024). Large legal fictions: Profiling legal hallucinations in large language models. Journal of Legal Analysis, 16(1), 64–93. https://doi.org/10.1093/jla/laae003
Davies, A., Olugasa, O., & Odunaike, D. A. (2024). Experimental trials of the virtual court proceedings in Nigeria: An empirical perspective. International Journal for Court Administration, 15(2), Article 4. https://doi.org/10.36745/ijca.550
Dhungel, A.-K. (2025). "This verdict was created with the help of generative AI...?" On the use of large language models by judges. Digital Government: Research and Practice, 6(1), 1–8. https://doi.org/10.1145/3696319
Fine, A., & Marsh, S. (2024). Judicial leadership matters (yet again): The association between judge and public trust for artificial intelligence in courts. Discover Artificial Intelligence, 4(1), Article 44. https://doi.org/10.1007/s44163-024-00142-3
Guitton, C., Druta, V., Hinterleitner, M., Tamò-Larrieux, A., & Mayer, S. (2025). Adoption of artificial intelligence in the judiciary: A comparison of 28 advanced democracies. Discover Artificial Intelligence, 5(1), Article 169. https://doi.org/10.1007/s44163-025-00311-y
Hastings, C., Cotterell, A., & Bruce, F. (2026). Artificial intelligence and access to justice at the 'shop front': The potential and limitations of meeting legal need through technology. Australian Journal of Social Issues. Advance online publication. https://doi.org/10.1002/ajs4.70115
Hendrickx, V. (2025). The judicial duty to state reasons in the age of automation? The impact of generative AI systems on the legitimacy of judicial decision-making. Erasmus Law Review, 17(3), 36–48. https://doi.org/10.5553/ELR.000275
Henning, A., & Langenbach, P. (2026). Bridging the human– AI fairness gap: How providing reasons enhances the perceived fairness of public decision-making. Journal of Empirical Legal Studies, 23(1), 39–59. https://doi.org/10.1111/jels.70019
Hochstedler Webb, K. A., Riley, S. A., & Wells, M. T. (2026). An assessment of racial disparities in pretrial decision-making using misclassification models. Journal of Empirical Legal Studies, 23(2), 246–269. https://doi.org/10.1111/jels.70031
Kaplina, V. (2026). Chief Justice Robots: Turning the world into a sci-fi scenario. Ratio Juris, 39(1–2), 37–50. https://doi.org/10.1111/raju.70029
Katsh, E., Schwartz-Maor, T., & Rabinovich-Einy, O. (2025). AI and the future of disputing: Naming, blaming, claiming, and preventing. Conflict Resolution Quarterly, 43(1), 79–89. https://doi.org/10.1002/crq.21477
Kim, T., & Peng, W. (2025). Do we want AI judges? The acceptance of AI judges' judicial decision-making on moral foundations. AI & Society, 40(5), 3683–3696. https://doi.org/10.1007/s00146-024-02121-9
Knes, A. S., Lowder, E. M., Thai, M. L., Reuter, S. M., & Kent, A. R. (2025). Multi-study examination of criminal-legal professionals' use of risk assessments in pretrial decision-making. Legal and Criminological Psychology, 30(2), 268–297. https://doi.org/10.1111/lcrp.12305
Law-Smith, M. (2026). Algorithmic sentencing and human dignity. Criminal Justice Ethics, 45(1), 70–92. https://doi.org/10.1080/0731129X.2026.2644706
Magesh, V., Surani, F., Dahl, M., Suzgun, M., Manning, C. D., & Ho, D. E. (2025). Hallucination-free? Assessing the reliability of leading AI legal research tools. Journal of Empirical Legal Studies, 22(2), 216–242. https://doi.org/10.1111/jels.12413
Martinho, A. (2025). Surveying judges about artificial intelligence: Profession, judicial adjudication, and legal principles. AI & Society, 40(2), 569–584. https://doi.org/10.1007/s00146-024-01869-4
Nielsen, A., Skylaki, S., Norkute, M., & Stremitzer, A. (2024). Building a better lawyer: Experimental evidence that artificial intelligence can increase legal work efficiency. Journal of Empirical Legal Studies, 21(4), 979–1022. https://doi.org/10.1111/jels.12396 Söderlund, K. (2025). High-risk AI transparency? On qualified transparency mandates for oversight bodies under the EU AI Act. Technology and Regulation, 2025, 97–113. https://doi.org/10.71265/6bedar76 Tamošiūnienė, E., Terebeiza, Ž., & Doržinkevič, A. (2024). The possibility of applying artificial intelligence in the delivery of justice by courts. Baltic Journal of Law & Politics, 17(1), 223–237. https://doi.org/10.2478/bjlp-2024-0010
Wu, W., & Lin, X. (2025). Access to technology, access to justice: China's artificial intelligence application in criminal proceedings. International Journal of Law, Crime and Justice, 81, Article 100741. https://doi.org/10.1016/j.ijlcj.2025.100741
Wunti, U. I., Ibrahim, A., & Darazo, U. B. (2026). Generative artificial intelligence and the administration of justice in Nigeria: Opportunities, challenges and a framework for expeditious justice delivery. International Journal of Law, Politics and Humanities Research. https://doi.org/10.70382/caijlphr.v12i6.0122
Zeleznikow, J. (2023). The benefits and dangers of using machine learning to support making legal predictions. WIREs Data Mining and Knowledge Discovery, 13(4), Article e1505. https://doi.org/10.1002/widm.1505 Cases and legislation Administration of Criminal Justice Act 2015 (Nigeria). African Charter on Human and Peoples' Rights, adopted 27 June 1981, entered into force 21 October 1986, arts. 7 and 26. Ayinde v London Borough of Haringey; Al-Haroun v Qatar National Bank QPSC [2025] EWHC 1383 (Admin). Constitution of the Federal Republic of Nigeria 1999 (as amended), ss. 36 and 294. Evidence Act 2011 (Nigeria), as amended by the Evidence (Amendment) Act 2023. Mata v Avianca, Inc., 678 F. Supp. 3d 443 (S.D.N.Y. 2023). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act), Annex III, point 8. Official Journal of the European Union, L, 2024/1689, 12 July 2024. State v Loomis, 881 N.W.2d 749 (Wis. 2016).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 IIAR Journal of Law, Ethics, and Policy
This work is licensed under a Creative Commons Attribution 4.0 International License.
