Artificial Intelligence in the Courtroom: What Every Expert Witness Must Know
Artificial Intelligence (AI) is a rapidly evolving technical phenomenon that will revolutionize many aspects of our lives. Algorithms are already being used in a variety of applications, from self-driving cars to personalized healthcare, and its impact is only expected to grow in the coming years.
The current state of AI is already impressive, but there is still a long way to go before it reaches its full potential. This assumes, of course, that there is any limit to that potential.
In assessing the impact that AI is having on various occupations, our systems of criminal and civil justice are no exception. In fact, the potential for AI to revolutionize how cases are evaluated, prepared, prosecuted, and defended is nothing short of mind-numbing.
It stands to reason, then, that AI will also weigh heavily on the use and scrutiny of expert witnesses in litigation and, therefore, how those experts gather and apply their expertise in the resolution of disputes, whether they reach the courtroom or not.
Before we delve into the specific implications of AI on the enterprise of expert witnessing, let’s review just a few specific examples of how AI is already being integrated into our existing legal and judicial processes:
Bias Mitigation – AI can potentially reduce the risk of bias in legal proceedings. For example, AI-powered decision-making tools can be used to eliminate unconscious bias in jury selection and ensure that decisions are based on objective criteria. AI can also help to eliminate bias in legal research by ensuring that lawyers have access to the most relevant information, regardless of their personal opinions.
Contracts – AI algorithms can scan contracts and identify areas of risk, saving lawyers time and effort in the process. AI can also help identify key terms and clauses in contracts, making it easier for lawyers and their clients to understand and interpret the agreement.
Data analysis – AI algorithms can analyze data from previous cases to make predictions about how a legal strategy will likely fare in a particular case. AI can also assist in the prediction of legal outcomes by analyzing the behavior of the parties involved and estimate the likelihood of a successful outcome based on that behavior.
E-Discovery – AI algorithms can help lawyers review and identify relevant documents from massive amounts of electronic data, saving time and reducing the cost of both the discovery process and the litigants preparation for trial. This technology can also identify patterns and relationships between various pieces of information, making it easier for lawyers to build a case. Additionally, AI can automate tasks such as document classification, tagging, and summarization, freeing up lawyers to focus on more complex tasks.
Evidence Analysis – AI can be used to analyze audio and video recordings, images, and other forms of digital evidence to extract relevant information. This can be especially useful in cases involving complex technical evidence, where human analysis may be slow and prone to errors.
Legal Research – Legal research can be time-consuming and costly, but with the use of AI, this process can be simplified. AI-powered legal research tools can quickly and accurately analyze large amounts of data, including legal documents, case law, and statutes, to provide lawyers with relevant information. This not only saves considerable time and money, but also ensures that lawyers have access to the most up-to-date information, which is essential for making informed decisions in a case.
Predictive Analytics – AI is already impacting litigation practices through predictive analytics that use “machine learning” algorithms to analyze data and predict future outcomes. This technology can be applied in the courtroom to predict the likelihood of a case being settled, the likelihood of a judge ruling in favor of one party, and the likelihood of a case going to trial.
Artificial Intelligence and Expert Witnessing
The admissibility of expert testimony is based on established rules of evidence. Federal Rule of Evidence 702, more specifically, is a rule of evidence that governs the admissibility of expert testimony in federal court proceedings. This rule sets out the standards for the admission of expert testimony in federal court, including the qualifications of the expert witness, the reliability of the expert’s testimony, and the relevance of the expert’s testimony to the case at hand.
State courts have their own rules of this sort and are based, with few exceptions, on Federal Rule 702.
Under Rule 702, an expert witness may provide testimony if the witness is qualified by knowledge, skill, experience, training, or education, and if the testimony is both reliable and relevant to the matter at hand. The reliability of the expert’s testimony is determined by considering factors such as the expert’s methodology, the peer-review process for the expert’s work, and the general acceptance of the expert’s methods and techniques.
The relevance of the expert’s testimony is then determined by considering whether the testimony will help the trier of fact understand the evidence or determine a fact in issue. The expert’s testimony must be more probative than prejudicial, meaning that the testimony should be helpful to the trier of fact without unfairly influencing the outcome of the case.
Thanks to the clarifications set forth under Daubert (1993), judges play the most critical role in determining the admissibility of expert testimony in court. Artificial intelligence can be used as a tool to assist in this “gatekeeping” role.
Below are some ways in which AI can – and it likely will – be used to determine the admissibility of expert testimony:
Assessing qualifications – AI can be used to assess the qualifications of an expert witness, such as their education, experience, and publications. This information can help a judge determine if the expert is qualified to provide testimony in their field of expertise.
Evaluating consistency – AI can be used to evaluate the consistency of an expert witness’s testimony over time. The AI system can compare the witness’s testimony in previous cases to her current testimony and flag any inconsistencies. This information can help a judge determine the reliability of the expert witness’s testimony.
Identifying potential biases – AI can be used to identify potential biases in an expert witness’s testimony. For example, if an expert witness has a financial relationship with a party in the case, AI can flag this information and provide it to the judge, who can then consider the potential impact on the witness’s testimony.
Evaluating credibility – AI can analyze the witness’s testimony and flag any statements that are inconsistent with established facts or lack sufficient support. This information can help a judge determine the reliability of the expert witness’s testimony.
Predicting outcomes – AI can be used to predict the outcomes of expert testimony based on historical data and patterns. A properly designed and coded AI system can analyze the testimony of similar experts in previous cases and predict the likelihood that a particular expert’s testimony will be deemed admissible. This information can be used to assist a judge in determining admissibility.
Among the more controversial implications of AI in assessing the admissibility of expert testimony will be the tendency of judges to place limits on what the court considers to be permissible testimony.
To the extent that AI can provide the court and its officers with reliable information about any topic, we may see tighter constraints placed on experts in court.
Consider the following example. Using the ChatGPT writing algorithm, the following summary was provided as an answer to this question:
What are the most likely causes of DNA contamination in criminal investigations?
Here is the unique, unedited answer that ChatGPT produced:
DNA contamination is a common issue in criminal investigations and can have serious implications for the integrity and accuracy of forensic evidence. The following are some of the most likely causes of DNA contamination in criminal investigations:
Laboratory contamination: This occurs when samples become contaminated during the collection, processing, or storage of evidence. For example, a sample may become contaminated by the DNA of laboratory personnel, or by DNA from other samples being processed in close proximity.
Cross-contamination: This occurs when samples become mixed with other samples during the collection or processing of evidence. For example, cross-contamination may occur when a swab used to collect evidence from one crime scene is reused to collect evidence from another crime scene.
Inadequate cleaning of equipment: This occurs when laboratory equipment is not properly cleaned between uses, leading to the transfer of DNA from one sample to another.
Improper handling of evidence: This occurs when evidence is not handled in a manner that minimizes the risk of contamination. For example, failure to wear gloves or to change gloves between collecting samples can lead to contamination.
Human error: This occurs when laboratory personnel make mistakes during the collection, processing, or analysis of evidence. For example, a laboratory personnel may mix up samples or accidentally contaminate a sample with their own DNA.
In conclusion, DNA contamination is a serious issue in criminal investigations and can occur due to a variety of factors, including laboratory contamination, cross-contamination, inadequate cleaning of equipment, improper handling of evidence, and human error. It is important for forensic laboratories to implement strict protocols to minimize the risk of DNA contamination and ensure the accuracy and integrity of forensic evidence.
As AI becomes increasingly accurate, it is conceivable that some or all courts will object to experts testifying about matters that the court can determine for itself through AI. This may be a long way off, but it’s clearly the direction we are moving.
Whether or not the answer provided by ChatGPT is 100% accurate or only partially accurate is not material to this discussion anyway. What matters is that the above information provides a sturdy rhetorical platform from which to develop challenging questions that will test an expert witness’s knowledge.
The Risks of Courtroom AI
As we explore the powerful benefits of artificial intelligence in legal proceedings, it is important to acknowledge the risks. These include:
- Bias – AI algorithms are trained on data and if that data contains biases, the algorithm will learn and reflect those biases. This could result in AI making incorrect or unfair assumptions in a courtroom setting. For example, if an AI system is trained on data that contains racial biases, it may make decisions that unfairly discriminate against certain racial groups.
- Lack of transparency – AI algorithms are complex and often operate on “black box” principles, meaning that it is difficult to understand how the algorithm arrived at a particular decision. This lack of transparency can make it difficult to determine whether an AI-generated decision is fair and just, or if it is based on incorrect or biased data.
- Lack of accountability – If an AI system produces inaccurate information, it can be difficult to determine who should be held responsible for the error. This lack of accountability can make it challenging to hold the appropriate parties accountable for any harm caused by the AI system.
- Legal challenges – The use of AI in a courtroom setting can raise legal questions regarding admissibility of AI-generated evidence and the qualifications of AI systems, to the extent they serve as expert witnesses themselves. There may also be concerns about the use of AI in determining guilt or innocence, which could impact the rights of defendants.
- Technical issues – AI systems are complex and can be prone to technical glitches, such as software bugs or hardware failures. If an AI system fails in a courtroom setting, it could result in delays or incorrect interpretations being made during the litigation process.
It has become clear that AI can be a useful tool for judges in determining the admissibility of expert testimony. AI can assist in assessing qualifications, managing large volumes of data, evaluating consistency, identifying potential biases, evaluating credibility, and predicting outcomes. It is important to note, however, that the use of AI in determining the admissibility of expert testimony is still in its early stages and will undergo extensive development and refinement in the coming decades.
I wish to point out that most of this article was laced together by combining four essays written by ChatGPT, which took it less than one minute to produce. As with most AI systems, this it not what I would describe as a well-written article; the syntax and tone of the writing was raw and lacked editorial sophistication in its original form. It took me about 60 minutes to do make the edits and additions needed to make this piece presentable for an article of this sort.
As an author, I hold myself accountable for disclosing and citing any language produced by an AI system, which I consider to be an independent author, even though it is not a live person. dBut to write an article of this length and complexity without the assistance of AI would have taken me at least eight to ten hours of research, writing, and editing.
A new day has surely dawned in our pursuit of justice, and every expert witness must be ready for it. “Artificial expertise” is here to stay.
John M. Collins is an executive trust coach at Critical Victories in Southfield, Michigan. He specializes in supporting clients in authoritative, high-stakes occupations requiring high levels of expertise to earn and retain the trust of the public or other consequential stakeholders, including forensic science, law enforcement, and law practice. John shares some of his unique philosophies and insights on high-stakes leadership in his 2022 book, THE NEW SUPERIOR – A BETTER WAY TO BE THE ONE IN CHARGE (www.thenewsuperior.com), available in hardcover and audio.
John works with people, teams, and organizations across the United States and oversees. If you are serious about expanding your leadership effectiveness, click below to request a free client strategy call: