The Verification-Value Paradox: A Normative Critique of Gen AI in Legal Practice

TL;DR

This paper critiques the optimistic view of generative AI in legal practice, highlighting risks related to AI's disconnection from reality and the need for rigorous verification to uphold legal integrity.

Contribution

It introduces the verification-value paradox, a new framework emphasizing verification over efficiency in AI-assisted legal work, and discusses its implications for legal practice and education.

Findings

Increased AI use necessitates greater manual verification of outputs.

AI's lack of transparency challenges legal duties of honesty and integrity.

Efficiency gains may be offset by verification requirements.

Abstract

It is often claimed that machine learning-based generative AI products will drastically streamline and reduce the cost of legal practice. This enthusiasm assumes lawyers can effectively manage AI's risks. Cases in Australia and elsewhere in which lawyers have been reprimanded for submitting inaccurate AI-generated content to courts suggest this paradigm must be revisited. This paper argues that a new paradigm is needed to evaluate AI use in practice, given (a) AI's disconnection from reality and its lack of transparency, and (b) lawyers' paramount duties like honesty, integrity, and not to mislead the court. It presents an alternative model of AI use in practice that more holistically reflects these features (the verification-value paradox). That paradox suggests increases in efficiency from AI use in legal practice will be met by a correspondingly greater imperative to manually verify any outputs of that use, rendering the net value of AI use often negligible to lawyers. The paper then sets out the paradox's implications for legal practice and legal education, including for AI use but also the values that the paradox suggests should undergird legal practice: fidelity to the truth and civic responsibility.