In this interview with the Silicon Valley Arbitration & Mediation Center, Rashid Muhamedrahimov, Director of Cornerstone Researach’s Applied Research Center, discusses the use of AI in expert evidence work.
Judgment: Now and Future
Where is AI actually being used in expert work today?
“To simplify greatly, you can think of there as being two ends to the spectrum of work. One is high-judgment, high-expertise work: which arguments are most compelling, whether a statistical model matches the economic theory and the facts of the market. The other is implementation and execution, such as document formatting. AI is an incredible set of technologies for doing that execution work much more quickly. Instead of writing the code that cleans a dataset, you spend the time on which hypotheses to test and what is interesting in the data.” Still, even the execution end is full of judgment calls: “When you clean a client’s data and a field has a missing observation, do you drop it, or assume it is zero? AI will happily make that call for you. Whether it is the right call, for the question you are answering, is a human judgment.”
Will the increasing competency of AI “encroach” on the number of judgment calls available for humans?
“It would be an incorrect framing to think there is a fixed amount of work, where AI takes over more of the small judgment calls, and we are left with less. The number of judgment calls goes up, because we can execute on so much more: analyze more data, ask more questions. Everyone will be able to produce more output. The skill of understanding all those analyses, and how they fit the question and the industry, only matters more.”
Quality Control and Verification
Expert reports are scrutinized down to the tiniest detail. What measures can safeguard against AI-driven errors?
“Quality control should be built into any AI tool use. We never just let the AI do a task and run with the output. In document review, say, sample from the set, have a human label the sample, and check the AI output against it, or have agents cross-check other AI tools. But the most important part is the judgment of practitioners: knowing to what extent to trust different levels of AI output, and how to verify them. That is an organizational skill, and I think it will be a big differentiator between firms. With responsible use, with people who own the output and are qualified to own it, under the scrutiny of the other side, we have an opportunity to level up the work we do. The downsides are manageable. They should not stop us doing something good.”
Impact for Young Professionals
Might frictionless AI stunt the development of junior analysts?
“Whether removing friction is good depends on whether what you would have learned from it is still relevant in a world of agentic AI. The syntax of code matters much less now. What matters as much as it ever did is the logical steps you take to transform or interpret a dataset. So you learn those foundations, you eat your vegetables, and then you work in a hybrid way, pair programming with AI rather than pure vibe coding.”
The Cost of Opting Out
Is it ever irresponsible for a firm not to use AI?
“I won’t comment on irresponsibility. That is a value judgment. But suppose a firm says, let’s not use AI, because even used carefully it mislabels documents, say, half a percent of the time. You miss out on all the ways AI expands what you can do: the analyses you run, the data you can handle, the hypotheses you can test. We will always use tools in a way where we are confident the outputs are careful and reliable. But by using AI we can provide better work more quickly, and that is what clients want to pay for. To leave that on the table because of a very mitigable risk is probably not the right business decision.”
London
