Can Expert Witnesses Use AI?

As artificial intelligence is increasingly integrated into professional fields, its potential use by expert witnesses in legal proceedings raises critical questions about reliability, trustworthiness and transparency. While expert witnesses have long been essential to helping courts interpret complex data, incorporating AI into their analyzes brings new complexities.

How can these experts rely on the knowledge generated by AI when the inner workings of the technology often remain obscure and difficult to explain? If AI’s lack of reproducibility undermines an expert’s testimony, what are the implications for justice and fair outcomes in the courtroom?

What is an expert witness?

In legal proceedings, experts play a key role in shaping the outcome of cases by providing specialized knowledge that helps the court understand complex evidence or technical issues.

An expert is a person who, because of his expertise, experience and knowledge in a certain field, is called upon to give opinions that go beyond the knowledge of lay people or the court itself. Whether the case involves forensic science, medical evaluations, financial analysis, or digital forensics, expert witnesses provide insights that can clarify the facts and influence legal decisions.

Expert witnesses are critical in helping judges and juries understand complex technical or scientific information. Their testimony often bridges the gap between expert evidence and legal reasoning, ensuring that complex evidence is presented in a way that is understandable and relevant to the case. The opinions they offer must be based on well-established principles, methodologies, and facts, all of which must be transparent and reproducible to hold up under questioning.

This makes reliability and trustworthiness the cornerstones of expert testimony. If an expert cannot explain the methodology behind their conclusions or if their findings cannot be replicated, their testimony risks being dismissed or discredited.

Credibility of the expert witness

Credibility refers to the trustworthiness of the expert. This includes not only their professional qualifications and reputation, but also how they present their findings in court. For example, if an expert can clearly explain the methodology used to reach their conclusions, showing that they followed recognized scientific methods and industry standards, their testimony will be seen as more credible.

On the other hand, if the expert cannot articulate the reasoning or process they followed, or if they rely on unproven or unaccepted methods in their field, their credibility is diminished. Credibility also depends on the demeanor, honesty and impartiality of the expert, as any sign of bias or uncertainty can undermine the strength of their testimony.

Credibility of the expert witness

Credibility relates to the consistency and accuracy of the expert’s findings. It refers to whether the expert’s methodology can be trusted to produce consistent and repeatable results. In the scientific and forensic fields, the ability to repeat results using the same methods under the same conditions is a hallmark of reliability.

If an expert’s findings cannot be replicated—if they rely on processes that produce inconsistent results—then those findings can be considered unreliable. In legal contexts, reliability also includes adherence to established principles and the scientific method, ensuring that the evidence and conclusions presented are sound and verifiable.

AI and expert opinions

For experts who may need to testify in court or provide professional opinions based on knowledge generated by AI, the technology presents a serious challenge: the lack of transparency and repeatability in AI processes.

In any scientific or legal context, the essential principle of evidence is that it must be reproducible. When an expert witness takes the stand, their testimony must be based on clear and reproducible methods. For example, a forensic pathologist needs to explain the steps taken during an autopsy, or a financial analyst needs to detail how they arrived at a valuation. Similarly, in digital forensics, the investigator must demonstrate that they followed a consistent and repeatable process to obtain their findings.

AI complicates this because many of its processes operate as “black boxes”. Even experts using AI tools may not fully understand how the algorithms reached a certain conclusion. If an AI model is asked the same question on different occasions, it may produce different results based on evolving data inputs or internal adjustments. This unpredictability introduces uncertainty, undermining the credibility of expert opinions if they are based solely on AI-generated data.

The black box problem

In a courtroom, an expert’s testimony often plays a key role in shaping the outcome of a case. An expert’s conclusions must be clear, defensible and reproducible. However, if the AI ​​tool they use is not transparent, or if it generates different results when presented with the same data at different times, it raises significant concerns about the integrity of their testimony.

Courts and regulatory bodies require that the methods used to reach conclusions are fully understood and open to scrutiny. If an AI algorithm yields conclusions that the expert cannot explain because the AI ​​operates based on complex neural networks or evolving datasets, the credibility of the evidence is called into question.

Reproducibility and scientific rigor

The basis of scientific evidence is repeatability. Forensic experts, for example, must be able to recreate the steps taken in their investigation and demonstrate that under the same conditions, anyone using the same methodology would reach the same result.

However, AI presents a challenge to this concept. While AI excels at processing and analyzing large amounts of data quickly, it doesn’t always produce consistent results when fed the same input many times. AI models are often dynamic, constantly adjusting as they process more data, which can make them less predictable and harder to validate. Without a clear picture of how the AI ​​reached its conclusions, experts are left in a difficult position. They cannot reliably replicate the AI ​​process and may struggle to defend its results under cross-examination in court.

This lack of transparency can undermine the credibility of any expert testimony that relies on AI. Whether it’s a healthcare professional providing a diagnosis based on AI tools or a financial analyst predicting market trends, if the underlying AI cannot be explained or replicated, the expert’s opinion can be questioned, putting entire cases or decisions at stake.

How should expert witnesses use AI?

Experts should approach AI with caution, making sure they understand how the technology works and validate the results it produces before relying on it to form professional opinions or testimony. In particular, the use of AI in courtrooms is subject to standards that require transparency, repeatability, and reliability—qualities that AI, especially complex models like machine learning systems, may not always easily provide. Here’s how experts should approach using AI:

Understand Technology

Experts must have a deep understanding of the AI ​​tools they are using. AI operates on algorithms that process data to generate insights, but if an expert doesn’t understand how the AI ​​reached a conclusion, they can undermine the credibility of their findings. Knowing the strengths, weaknesses and limitations of AI systems is essential. For example, machine learning algorithms can learn from data and change over time, which can lead to different results for the same input.

Use AI as a complement, not a replacement

AI should be used to assist experts in their decision-making process, not to replace them. Experts must verify and interpret AI results within the context of their knowledge and experience. For example, in fields such as medicine or finance, AI may provide diagnostic or predictive knowledge, but the final decision must always rest with the human expert, who may consider other factors that AI may not.

Prioritize Transparency and Explainability

When using AI, especially in legal contexts, experts should choose tools that provide transparency and explainability. Black-box AI models that provide little or no insight into how decisions are made pose a risk, especially in courtrooms where transparency is essential. Experts must be able to explain the AI ​​process and ensure that its findings are reproducible and defensible under scrutiny.

Validate AI results

No matter how sophisticated the AI ​​tool is, experts must validate its findings. Artificial intelligence can help process large amounts of data and identify potential evidence, but the expert must verify the findings to ensure they meet forensic standards. Relying on AI results without thorough verification can lead to wrong conclusions.

What does responsible use of AI look like for experts?

As an expert in digital forensics, I know that AI cannot be the final decision maker because that responsibility falls on me. Experts should treat AI as a tool for uncovering potential evidence, but always validate findings independently.

AI can identify flag patterns or anomalies, but the human expert must decide what constitutes evidence and how it should be interpreted and presented in court. Ensuring that AI results are consistent, repeatable and supported by human analysis is essential to maintaining the integrity of forensic investigations.

In summary, AI offers tremendous potential to improve efficiency and accuracy in various expert fields, especially in data-intensive fields such as digital forensics. However, the ultimate responsibility rests with the expert to ensure that AI contributions are transparent, explainable and substantiated, especially when the expert’s testimony in court or regulatory compliance is involved.

The stakes are very high

In a courtroom, the stakes can be extremely high. Lives, reputations and financial futures are often on the line. When expert witnesses testify, it is often crucial in determining the outcome of a case. A misstep in handling AI-generated insights, or a failure to properly validate AI findings, can mean the difference between a fair trial and a miscarriage of justice.

In criminal trials, forensic evidence can determine guilt or innocence, directly affecting someone’s liberty or even their life. In civil cases, expert testimony can affect significant financial outcomes, affecting people’s livelihoods or businesses. The growing role of AI in these processes underscores the importance of ensuring that all findings, whether AI-assisted or otherwise, are transparent, reproducible and verifiable. If experts cannot reliably explain and validate their AI-generated conclusions, the risk is not only professional discredit; it can lead to wrongful convictions and unjust sentences.