Setting Realistic Standards for AI in Legal Practice

Evaluating AI performance in legal tasks requires thoughtful metrics. Without appropriate assessment frameworks, we risk holding automated systems to standards beyond what we expect from human attorneys. Many demand perfection from AI tools, and while excellence should be a goal, this unrealistic expectation can mask the practical value these systems offer today and delay their adoption. When assessing AI in legal settings, comparing it to how humans perform makes more sense than demanding flawlessness. This lesson has already been demonstrated with technologies like eDiscovery.

The Human Baseline and Better Benchmarks

Attorneys make mistakes. An incorrect page citation, misstated legal principle, or overlooked procedural requirement—these aren't necessarily signs of poor lawyering but rather unavoidable aspects of human work. Our legal system has evolved with this understanding, incorporating safeguards like adversarial review and professional standards that emphasize reasonable competence rather than perfection.

Take eDiscovery as an example. Early research showed human document reviewers missed relevant materials at rates approaching 25%. This wasn't a new problem—it was simply the first time we measured what practitioners already understood: human review has inherent limitations. The profession adapted by creating workflows that account for these shortcomings while maintaining quality and fairness.

If we don't expect perfection from human lawyers, why demand it from AI? A more practical approach directly compares AI performance to typical human results on the same tasks. This perspective shifts the question from "Is the AI output perfect?" to "Does the AI work as well as a reasonably capable attorney?"

The adoption of machine learning in eDiscovery illustrates this well. AI document review tools gained acceptance not because they achieved perfection in document review, but because their accuracy matched or exceeded human reviewers while processing documents more quickly and consistently. Courts accepted this comparative standard, recognizing that technology doesn't need to be flawless to be defensible.

The Value of Human-AI Collaboration

Perhaps the most useful evaluation framework examines whether attorneys working with AI produce better outcomes than attorneys working alone. This approach acknowledges that the lawyer maintains responsibility for the final work product while recognizing AI as a valuable enhancement tool.

This collaborative model addresses accountability directly. The attorney retains responsibility for verifying AI output and making final judgments, just as they would when supervising junior associates.

In eDiscovery, once practices developed that combined human oversight with AI capabilities, the results became clear. Litigation proceeded more efficiently and affordably while improving the overall quality and consistency of document review. This experience offers guidance for integrating AI into other areas of legal practice.

Demanding immediate perfection from AI establishes an unreasonable standard that human lawyers themselves don't meet. Our legal system has functioned for centuries while acknowledging human fallibility and incorporating appropriate safeguards. AI presents an opportunity to enhance these existing systems, not replace them with something flawless.

The most promising approach involves thoughtful partnership between lawyers and AI, guided by realistic performance expectations. By focusing on verification processes, quality control measures, and comparative benchmarks, the legal profession can benefit from AI while preserving the judgment and accountability that characterize effective legal practice.

The question isn't whether AI will be perfect, but whether it can help lawyers serve clients better than they could alone. The evidence from eDiscovery and emerging applications suggests that the answer is an emphatic yes, provided we implement these tools with appropriate expectations and safeguards.