Human Judgment, Machine Intelligence: Why AI Cannot Replace Legal Reasoning But Can Transform The Backbone Of Legal Practice
- IJLLR Journal
- 4 minutes ago
- 1 min read
Shubham Paliwal, Guru Gobind Singh Indraprastha University
ABSTRACT
This research paper examines the fundamental distinction between computational efficiency and judicial reasoning, arguing that while algorithmic systems excel at processing legal information, the core function of legal judgment, resolving contested interpretations, balancing competing principles, and delivering justified decisions, remains epistemologically beyond machine capability. Drawing on contemporary neuroscience, philosophy of law, and empirical implementation data from India's judiciary (2024-2025), this paper establishes that AI's transformative value lies in liberating legal professionals from mechanical labor rather than automating judgment. The analysis identifies concrete failures in current AI deployment (hallucination incidents costing USD 50,000+ in sanctions), contrasts these with documented successes in administrative efficiency (productivity gains exceeding 100-fold), and proposes a governance framework distinguishing appropriate technological roles. Through detailed examination of India's e- Courts ecosystem, NCLT automation initiatives, and Kerala High Court's mandatory transcription protocols, this paper develops a pragmatic model for human-machine collaboration that preserves judicial integrity while maximizing institutional efficiency. The research concludes that sustainable legal technology deployment requires explicit recognition of three non- negotiable principles: (1) machines process information; humans interpret meaning; (2) technology amplifies capacity but cannot replicate judgment; (3) accountability requires transparency that current generative systems cannot provide.
Keywords: Legal Interpretation, Machine Learning Limitations, Judicial Efficiency, Human-Machine Collaboration, India Legal Technology, Epistemology of Judgment
