How to Get Around the AI Trust Paradox in Legal Practice

In a bustling law firm, trust isn’t just a five-letter word—it’s the very backbone of collaborative work. As a junior partner said at a recent AI task force, “After working with a four-year associate for a while, in time, I will trust the first draft of their work product enough to not spend too much time reviewing it. With generative AI, I will never develop that level of trust.” This captures a deeply entrenched challenge in the world of legal tech—the trust paradox.

Understanding the Sentiment

At the heart of this sentiment lies a complex cocktail of human emotion, experience, and intuition. As lawyers work together over time, they develop a mutual understanding. They get a feel for each other’s strengths, weaknesses, and quirks.

When a senior attorney reviews a document crafted by an associate they have collaborated with for years, they see the words on paper, and understand the human thought process behind them. This tapestry of shared experiences, understanding each other’s reasoning, and the occasional shared sigh over a late-night coffee builds a level of trust that’s hard to replicate.

With generative AI, the narrative changes. Even if the AI consistently produces impeccable drafts, there’s no shared journey, no late-night brainstorming sessions, no exchanged glances when a complex case gets a breakthrough.

The AI tool doesn’t evolve in its understanding in the way a human associate might. It remains, in essence, an enigma, producing outcomes without the rich tapestry of human experiences. This abstract nature of AI becomes a trust barrier.

Addressing the Challenge

Human-AI Collaboration. One way to bridge this trust gap is through active human-AI collaboration. Instead of letting the AI draft in isolation, attorneys can work with it iteratively.

Feed the AI preliminary thoughts, get a draft, make edits, provide feedback, and then let it refine the document. This iterative process, over time, can instill a greater sense of control and predictability, fostering trust.

Transparent Algorithms. If attorneys understand the how and why behind AI decisions, they may feel more comfortable with the output. Offering transparency into how the AI model works, the data it was trained on, and the logic it employs can alleviate some trust concerns.

Consistent Quality Checks. While a junior partner might trust an associate’s draft, they still typically conduct reviews—it’s part of the legal due diligence. The same should apply to AI. Periodic quality checks, even if they decrease in intensity over time, can provide assurance of the AI’s reliability.

Feedback Loops. Just as human associates learn from feedback, sophisticated AI models can too. By incorporating feedback loops, where the AI learns from the corrections and preferences of the attorney, the system can continuously improve and align more closely with the lawyer’s expectations.

Empathy and Relatability. One of the things AI lacks is the human touch, the ability to empathize, and relate. Perhaps the solution doesn’t lie in making AI more human but in recognizing and appreciating it for what it is—a tool. Knowing the strengths and limitations of this tool can set realistic expectations and foster trust in its capabilities.

While the trust paradox is real, it isn’t insurmountable. With time, understanding, and consistent collaboration, even the chasm between human intuition and AI’s algorithms can be bridged.

After all, every good lawyer knows that a seemingly watertight argument can always find room for a counter, given the right perspective.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law and Bloomberg Tax, or its owners.

Author Information

Anup Iyer is a senior associate with Moore & Van Allen, focused on helping clients secure patent and trademark rights.

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