Abstract

The proliferation of AI tools in legal practice has outpaced the development of frameworks for evaluating, deploying, and governing those tools. Attorneys, law firms, and legal departments face a fragmented landscape of vendor claims, ethics opinions, court rules, and regulatory guidance that provides insufficient structure for making principled decisions about AI adoption. The result is a market characterized by both over-adoption — deployment of AI tools without adequate evaluation of their limitations — and under-adoption — avoidance of beneficial tools due to uncertainty about compliance obligations.

This paper introduces the AI Legal Reference Model (ALRM), a structured framework for evaluating and deploying AI tools in legal practice. The ALRM organizes the relevant considerations into five domains: Capability Assessment (what the tool can and cannot do), Risk Identification (what can go wrong and how likely), Compliance Mapping (what professional responsibility and regulatory obligations apply), Governance Design (what oversight structures are required), and Performance Monitoring (how ongoing quality is assured). Each domain is operationalized through a set of evaluation criteria and implementation protocols.

The ALRM is designed to be tool-agnostic and practice-area-neutral, applicable to AI tools ranging from document review platforms to generative AI research assistants to autonomous contract drafting systems. The paper provides worked examples of ALRM application across several practice contexts and concludes with recommendations for bar associations, law schools, and legal technology vendors seeking to promote responsible AI adoption in legal practice.

Full Paper

This paper is published on the Social Science Research Network (SSRN). To read the full text, download the PDF, or cite this work, please visit the SSRN abstract page:

→ Read the Full Paper on SSRN (Abstract ID: 6546398)

Published: April 2026 — Authors: Austin, Morris & Das — View all research papers