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| Tanvir Ahmed Tusher |
1.
Introduction
Artificial intelligence (AI) has
revolutionized the legal profession very rapidly, driving innovations in
contract review, litigation analysis, and risk assessment. From mundane
document checks to case outcome analysis, AI offers stunning productivity in
handling large containers of legal data. But while these instruments excel at
pattern recognition and consistency verification, they lack the essence required
for true legal interpretation. Law is more than a regime of rules and an
articulation of social values, cultural context, and moral judgment domains
that are not replicable by AI systems. AI lacks discretion, ethical weighing,
and interpretive judgment guided by changing norms, in contrast to human
jurists. This article argues that AI should still be a facilitator in legal
process, supporting and complementing human efforts rather than attempting to
replace the task of human interpreters. The test is to responsibly incorporate
AI to assist, not undermine, the human-centered principles of legal argument
and justice.
2.
What is Legal Interpretation?
Legal interpretation
is the reasoning process whereby judges, lawyers, and
scholars determine what legal documents statutes, constitutions,
or precedents mean and apply them to real cases.[1]
It is required because legal
language is often vague or broad in
nature, and human judgment is
needed to decide upon its application.[2]Two predominant schools
of thought guide it. Textualism places most emphasis
on the plain meaning of legal
words on the day of enactment.[3]
Purposivism looks beyond words to discern the law's intended goal.[4] Both traditions acknowledge, however, that
interpretation cannot be mechanical.Judges must reconcile conflicting statutes,fill in gaps inlegislations,and dispel ambiguities unintended by lawmakers.[5]
Importantly, legal interpretation incorporates moral reasoning, cultural
context, and social values, reflecting the idea that law operates within, not
above, society.[6]
This blend of discretion and principle ensures that legal outcomes align with
justice, rather than rigid formalism.[7]
3.
How AI Handles Legal Tasks
AI
in the practice of law primarily takes three shapes: expert systems, which
apply pre-defined legal rules; natural language processing (NLP) models, which
summarize and process legal text; and predictive analytics, which forecast
results based on historical data.[8]
They assist in tasks like contract review, risk analysis, and legal research. They
don't, however, interpret the law like humans do they look for patterns and
correlations without exercising principled reasoning.[9]
For instance, LawGeex’s
contract analysis AI has outperformed junior lawyers in spotting technical
risks, but it operates by matching text patterns to predefined rules, without
understanding broader contractual intent or fairness.[10]
Similarly, COMPAS, an American criminal court risk assessment tool, predicts
recidivism based on statistical models that are trained on historical data.
While useful, it has been criticized for being biased in perpetuation and
opaque in its decisions.[11]
Another
case in point, Aletras et al. trained a machine learning program to produce
some 79 percent of the judgments rendered by the European Court of Human Rights
(ECHR). This was achieved by text and fact permutation permutations correlated
with outcomes rather than reasoning through legal principles.[12]
These systems pose threats to fairness and accountability, especially in cases
of negative judgments or outcomes. In a majority of cases, AI is opaque in its
decision-making process, where the user may not know how or why a particular
conclusion was made, and AI cannot provide any moral justification for its
output.[13]
Thus, AI is best seen as a tool to support human judgment rather than replace
interpretative discretion.
4.
Where AI Falls Short
AI
lacks the ability to apply moral reasoning, understand cultural context, or
engage with evolving legal norms elements that are indispensable for true legal
interpretation.[14] Juridical
decisions are not merely statistical exercises; they arise from human value
systems and social expectations. The systems of AI are data driven; they cannot
reason morally or take context into consideration: they work with patterns; they
cannot, however, judge if these patterns are fair or just.[15]AI
also struggles with genuine legal ambiguity. Legal cases often present
conflicts between statutes, constitutional principles, or precedents. Human
judges resolve these using discretion, balancing competing values and societal
interests.[16]
In contrast, AI cannot navigate conflicting legal directives or interpret
ambiguous language beyond pattern matching.[17]
Many
AI systems are opaque or have a black-box character, and this adds to the
problems. The outputs of AI often cannot be meaningfully explained except in
terms a lawyer or judge could scrutinize. This negates established legal
principles of responsibility and sound judgment.[18]
Bias is another serious concern. AI systems like COMPAS, used in criminal
sentencing, have been shown to replicate and even amplify societal biases
embedded in training data.[19]
The operation has gained a bias, with undue benefits for these groups, while
raising it to the level of constitutional concern wherein equality and justice
have been impugned. Given the shortcomings, it stands unversed to go ahead and
interpret the law. Overblowing this idea runs the risk of replacing legal
reasoning with statistical outputs, hence jeopardizing the credible trust of
the public and the trustworthiness of the law itself.[20]
5.
The Future: AI as an Assistant, Not Interpreter
AI’s
role in law should centre on supporting human expertise rather than replacing
it. AI tools excel in legal research, pattern recognition, consistency
checking, and flagging potential anomalies or risks in documents.[21]
They acknowledge, tap into, and use AI's capacities to store and efficiently
retrieve vast data. However, the decisions are not really any form of concrete
decision-making. The discretion, moral reasoning, and final interpretive
authority should remain entirely with the human agents to ensure that the legal
conclusion will be in line with considerations of justice, equity, and
ever-evolving societal values.[22]
A
promising model is the human-in-the-loop
(HITL) system, where AI assists but humans retain oversight and
ultimate decision-making power.[23]
HITL frameworks help mitigate AI’s biases, errors, and ethical shortcomings,
fostering accountability.[24]
By way of this collaborative approach, AI should be brought in to augment
rather than undermine legality. Explainable AI (XAI) stands second in
importance. Without transparency, AI systems would end up eroding any remaining
trust in how laws are applied or outcomes. XAI is meant to interpret the AI
outputs into whatever remains human-useable so a lawyer could understand,
question, or justify AI-assisted results.[25]
This transparency is key to ethical AI integration in law.Future AI development
for legal contexts must incorporate safeguards: clear audit trails, external
oversight, regular bias assessments, and alignment with legal ethics
principles.[26]
These measures will ensure AI serves as a valuable tool, enhancing but never
substituting human legal reasoning.
6.
Conclusion
AI
technologies, being such powerful mechanisms for legal data processing and
pattern recognition, intrinsically lack the moral, cultural, and discretionary
capacities required in authentic legal interpretation. Law is not technical
rules—it involves living social values, justice, and human ethics impossible to
replicate by AI mechanisms. AI needs to be made to augment human judgment, and
not replace it, in the future. Its most notable contribution is in supporting
legal professionals with research, consistency checking, and issue spotting,
leaving moral reasoning and final interpretive decisions to humans. In order to
provide fairness and accountability, legal systems must make investments in
hybrid AI-human systems where AI augments but does not replace human
discretion. The future of AI in law depends on creating explainable,
transparent, and ethically grounded systems that support human-centered justice
rather than undermine it.
[1] B Watson, ‘What Are We Debating
When We Debate Legal Interpretation?’ (2025) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5149058
[2] M Tampubolon, ‘Decoding Legal Ambiguity:
the Interplay between Law and Legal Semiotics in Modern Jurisprudence’ (2025)
38 Intl J Semiotics L https://link.springer.com/article/10.1007/s11196-025-10271-2
[3] JL Perkins, ‘Speech Act Theory and
Textualism’s Unfaithful Agency Problem’ (2023) 48 Vt L Rev 455 https://lawreview.vermontlaw.edu/wp-content/uploads/2024/05/05-Perkins.pdf
[4] G Sullivan, ‘A Textualist Response
to Two Texts: Positive-Law Codification and Interpreting Section 1983’ (2025)
134 Yale LJ https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00440094&AN=184818628
[5] T Gizbert-Studnicki, ‘The
Separation Thesis and Legal Interpretation: An Overview’ (2024) Revus https://journals.openedition.org/revus/10856
[6] AD Silalahi et al, ‘Rethinking
Constitutional Interpretation through Joseph Raz’s Analytical Jurisprudence’
(2025) Const Rev https://consrev.mkri.id/index.php/const-rev/article/view/2167
[7] AS Krishnakumar, ‘Practical
Consequences in Statutory Interpretation’ (2025) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5168470
[8] D Ulusoy and D Ertuğrul, ‘Charting
New Frontiers: Artificial Intelligence Driving Sector Advancements’ in AI
and Digital Transformation (IGI Global 2025) https://www.igi-global.com/chapter/charting-new-frontiers/359015
[9] V Jadidi, ‘The Impact of
Artificial Intelligence on Judicial Decision-Making Processes’ (2025) Adv J
Management, Humanity and Soc Sci https://www.ajmhss.com/article_222612.html
[10] Ibid
[11] B Schafer, ‘Legal Tech and
Computational Legal Theory’ in B Schafer (ed), Autonomous Systems, Big
Data, IT Security and Legal Theory (Springer 2022) https://link.springer.com/chapter/10.1007/978-3-030-90513-2_15
[12] N Aletras and others, ‘Predicting
Judicial Decisions of the European Court of Human Rights: A Natural Language
Processing Perspective’ (2016) 2 PeerJ Comput Sci e93 https://peerj.com/articles/cs-93/
[13] V Jadidi (n 9)
[14] YW Chen, Plurality in
Artificial Intelligence Ethics: Through Collaborative and Democratic Approaches
(2025) https://dspace.cuni.cz/bitstream/handle/20.500.11956/197792/120488157.pdf
[15] Hein, KJ Nahra and R Cangarlu, The
Ethical Issues of Artificial Intelligence/Generative AI on the Practice of Law
in 2025 (2025) https://www.franchise.org/wp-content/uploads/2025/05/Paper-The-Ethical-Issues-of-Artificial-Intelligence_Generative-AI-on-the-Practice-of-Law-in-2025.pdf
[16] A Singh and J Rafiq, Implications
of Black Box Dilemma in the Indian Legal System (2025) https://jlrjs.com/wp-content/uploads/2025/06/111.-Amandeep-Singh.pdf
[17] S Chowdhury and L Klautzer, Shaping
an Adaptive Approach to Address the Ambiguity of Fairness in AI (2025) Cambridge
Forum on AI: Law and Governance https://www.cambridge.org/core/services/aop-cambridge-core/content/view/CDCFA55DD83FF4F674FE370FA657CCF7/S3033373325000079a.pdf
[18] S Solaimani and P Long, Beyond
the Black Box: Operationalising Explicability in Artificial Intelligence
(2025) Int J Business Information Systems https://www.inderscienceonline.com/doi/pdf/10.1504/IJBIS.2025.146837
[19] MTGB Hernández, Facing
Fundamental Rights in the Age of Preventive Ex Ante AI (2024) Deusto J
Hum Rts https://djhr.revistas.deusto.es/article/download/3191/3879
[20] T McMullen, Unconscious Bias
on the Implementation and Utilization of Emerging Technologies by Law
Enforcement Agencies (2025) https://search.proquest.com/openview/2f9a9f395faec0e93a162270e75f202c/1?pq-origsite=gscholar&cbl=18750
[21] BO Otokiti and others, ‘Developing
Conceptual AI Models for Legal Text Interpretation and Regulatory Compliance
Automation’ (2024) Multidisciplinary J https://www.allmultidisciplinaryjournal.com/uploads/archives/20250611124330_MGE-2025-3-281.1.pdf
[22] M Karayigit and D Çelikkaya, ‘The
Use of AI in Criminal Justice: Unpacking the EU's Human-Centric AI Strategy’
(2025) Nordic J Eur L https://journals.lub.lu.se/njel/article/view/27594
[23] A Arora, ‘Building Responsible
Artificial Intelligence Models That Comply with Ethical and Legal Standards’
(2025) SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5268172
[24] S Schmager, ‘Human-Centered
Artificial Intelligence: Design Principles for Public Services’ (2024) https://www.researchgate.net/publication/391646694
[25] M Bruijnes and S Grimmelikhuijsen,
Explainable AI Is No Silver Bullet (2025) https://library.oapen.org/bitstream/handle/20.500.12657/100827/9783031847486.pdf
[26] AOM Al-Dulaimi and MAAW Mohammed,
‘Legal Responsibility for Errors Caused by Artificial Intelligence in the
Public Sector’ (2025) Int J Law Management https://www.emerald.com/insight/content/doi/10.1108/IJLMA-08-2024-0295/full/html