Can AI Replace Human Mediators? Groundbreaking Study Reveals Surprising Results

[Editor’s Note: EDRM is proud to publish Ralph Losey’s advocacy and analysis. The opinions and positions are Ralph Losey’s copyrighted work.]

Artificial Intelligence is no longer just a tool for automating mundane tasks—it’s now stepping into arenas traditionally dominated by human judgment and empathy. One of the most intriguing applications of AI is in dispute resolution, where large language models like GPT-4 are being tested as mediators. Pre-trial settlements are critical to the continued functioning of our system of justice because an estimated 92% percent of civil cases are resolved out of court. With the rise of online dispute resolution, the potential for AI to resolve low-stakes disputes autonomously is appealing, especially as legal systems are increasingly overburdened with new case filings.

But can AI truly manage the complexities of human conflict? What happens when a machine has to balance neutrality with empathy, or data analysis with human emotion? This article discusses a groundbreaking study, “Robots in the Middle: Evaluating Large Language Models in Dispute Resolution,” offering insights into how AI may augment—though not replace—human mediators. Let’s explore the future of AI in the courtroom and beyond.

Introduction

The “Robots in the Middle“ study provides an empirical evaluation of LLM AIs acting as mediators. Mediation is very important to our system of justice because mediation and other methods of voluntary settlement keep our court systems functioning. According to an article by Harvard Law Professors, David A. Hoffman and John H. Watson, Jr: “… up to 92 percent of cases are resolved out of court, a figure that does not include the number of lawsuits that are never filed because the parties used other dispute resolution methods at the outset.” Resolving conflict outside the courtroom: Why mediation skills are increasingly valuable for lawyers, according to two Harvard Law experts (Harvard Law Today, 4/29/24). This is one reason why AI researchers in legal technology are so interested in the possible application of LLM AI to mediation.

The “Robots in the Middle“ study was based on mediation by text messages of disputes in fifty hypothetical disputes. The analysis and responses of humans with AI expertise and some limited legal experience, none of whom were professional mediators, were compared with responses of ChatGPT4o (omni). The AI prompts used in the experiment were based on the mediator’s guide of the Department of Justice of Canada. Dispute Resolution Reference Guide: Practice Module 2 (August 25, 2022). The humans and AI were asked to select among thirteen mediation types set forth by the DOJ Canada. They were asked to pick one to three types they judged to be appropriate for each hypothetical. Then they were asked to prepare text messages to facilitate a settlement. The authors served as blind judges to evaluate the quality of the responses, not knowing which were generated by humans and which by ChatGPT4o.

With the growing demand for Online Dispute Resolution (‘ODR”) platforms (see e.g. ODR.com), the study examines whether LLMs like GPT-4 might be able to effectively intervene in disputes by selecting appropriate types of mediation interventions and drafting coherent, impartial intervention messages.

The premise is simple, yet potentially transformative: if AI can handle routine, low-stakes disputes efficiently, this would alleviate the burden on human mediators, allowing them to devote their time and expertise to more complex, emotionally charged cases. The research sought to answer three fundamental questions:

1. How well can LLMs select intervention types in disputes?
2. How do LLMs compare to human mediators in drafting intervention messages?
3. Are AI-generated messages safe, free from hallucinations, and contextually appropriate?

Who is Behind this Study?

The “Robots in the Middle“ study has seven authors in academic fields of law and technology: Jinzhe TAN, Hannes WESTERMANN, Nikhil Reddy POTTANIGARI, Jaromır SAVELKA, Sebastien MEEUS, Mia GODET and Karim BENYEKHLEF. They are from multiple Universities: Cyberjustice Laboratory, University of Montreal, Canada; Maastricht Law and Tech Lab, Maastricht University, Netherlands; Mila – Quebec AI Institute, University of Montreal, Canada; School of Computer Science, Carnegie Mellon University, United States; and, Faculty of Law and Criminology, Universite Libre de Bruxelles, Belgium.

These are all brilliant international scholars with great expertise in legal theory and AI technology, but none appear to have any actual experience as a mediator or even experience serving as an attorney advocate in a mediation. Only one of the authors appears to have any experience with the U.S. legal system, Jaromır Savelka, who is a researcher associate at Carnegie Mellon. Savelka previously worked as a data scientist for Reed Smith (2017-2020). The lack of real legal experience in dispute resolution by the human subjects in the experiments is a weakness of this study.

Since my article is written primarily for U.S. attorneys and legal tech experts, I try to correct for this gap with input from a certified mediator, Lawrence Kolin, who has mediated thousands of cases of all types since 2001. He is also savvy in technology and AI. Moreover, I bring some specialized knowledge as an attorney who represented parties in many mediations since it first became a thing in Florida in the late 1980s. I was also trained and certified by the Florida Supreme Court as a Mediator of Computer disputes in 1989 but have never formally served as a mediator. That is why I sought the input and advice of a professional mediator included later in this article.

Background on the DOJ Canada Mediation Guide Used in the Experiment

I thought that the Mediation Summary prepared by the DOJ Canada was very good and a clever choice for the experimenters to use for guidance. I asked the latest version of Google’s Gemini generative AI, which seems to have improved significantly lately, to summarize the Mediation Guide. Dispute Resolution Reference Guide: Practice Module 2 (DOJ Canada, 8/25/22). I verified the accuracy and wording of the summary, which honestly was better than I could have done on this simple task, especially considering it took two seconds to prepare.

• Mediation is a voluntary and non-coercive process where a neutral third party assists disputing parties in reaching a mutually acceptable settlement. The mediator does not have the authority to impose a decision, but instead facilitates communication and negotiation.

• A successful mediation leads to a signed agreement or contract, often referred to as a memorandum of understanding, which outlines the parties’ future behavior and is legally binding.

• The mediation process offers several advantages:
  • Preserves Relationships: Mediation helps maintain relationships, especially when parties need to continue interacting, by focusing on shared interests and avoiding the adversarial nature of litigation.
  • Flexibility and Creativity: The informality of mediation allows for customized processes and solutions that cater to the specific needs and interests of the parties involved, going beyond traditional legal remedies.
  • Confidentiality: Mediations are generally private, except when subject to laws like the Access to Information Act and Privacy Act, ensuring discretion and protecting sensitive information.
  • Time and Cost Efficiency: Reaching a mediated settlement is typically faster and more cost-effective than litigation, benefiting both parties financially.
  • Controlled Dialogue: The presence of a neutral mediator enables a structured conversation, particularly helpful when emotions run high or previous negotiations have failed.
  • Shared Ownership: As the parties share the costs of mediation, they feel equally invested in the outcome and more committed to the process.

• The mediation process also has some potential drawbacks:
  • Power Imbalances: Concerns exist, especially in harassment cases, that power imbalances between parties could compromise the fairness of the process. Strategies such as co-mediation with mediators of different genders and legal representation can be used to address this.
  • Lack of Precedent: Due to its private and non-adjudicative nature, mediation does not establish legal precedents, unlike court judgments.
  • Mediator Influence: A dominant mediator might exert excessive control, potentially influencing the final resolution and undermining party autonomy.
  • Delay Tactics: The absence of a binding third-party decision may lead a party to engage in mediation without genuine intent to cooperate, using it as a stalling tactic

• The mediation process involves several key steps, which can vary depending on the specifics of the dispute.
  • Agreeing to mediate.
  • Understanding the problem(s).
  • Generating options.
  • Reaching agreement.
  • Implementing the agreement

• A successful mediation requires:
  • Good Faith Participation: All parties must actively and honestly participate in the process.
  • Impartiality of the Mediator: The mediator must remain neutral and avoid favoring any party.
  • Confidentiality: All statements and disclosures made during the mediation are generally considered confidential, subject to legal exceptions.

• The role of a mediator is to facilitate a productive and constructive dialogue between the parties, helping them identify their interests, explore options, and work towards a mutually acceptable agreement.

• Legal counsel can play a significant role in mediation by advising their clients, ensuring their interests are protected, and facilitating effective communication.

• The sources include a checklist and a sample mediation agreement that can be helpful resources for those considering or engaging in mediation.

Research Design and Key Findings

The study used 50 hypothetical dispute scenarios, designed to cover various real-world cases, from consumer complaints to more complex business disputes. These scenarios included emotional conflicts, deadlocked negotiations, and evidential disagreements. As mentioned, the LLMs were evaluated against humans acting as mediators in two critical tasks: selecting appropriate intervention types and drafting effective text messages to encourage settlement. The one-hundred AI responses (fifty in each category) were also evaluated for hallucinations or harmful errors (none found).

Selecting Intervention Types. The participants, AI and human, were instructed to select between one to three intervention types from a list of thirteen from the DOJ Canada Mediations Guide. Evaluators compared the intervention types chosen by humans and LLMs for each scenario, rating their preference on a 5-point Likert scale.

Evaluators preferred the LLM-chosen intervention types in 22 cases, were ambivalent in 9, and preferred human choices in 19. This suggests that LLMs are capable of comprehending dispute scenarios and selecting appropriate intervention types. The report concludes this shows that AI can understand dispute contexts and recommend suitable actions in a significant number of cases.

Drafting Intervention Messages. The participants were instructed to draft intervention messages of between one to two sentences. To allow for comparison in all fifty hypotheticals, the LLM was always instructed to generate messages based on the intervention types selected by the human annotator. Evaluators blindly assessed their preference for the intervention messages written by humans and LLMs, using a 5-point Likert scale and providing comments. They then compared the messages on specific criteria: understanding and contextualization, neutrality and impartiality, empathy awareness, and resolution quality.

LLM-generated messages were rated higher than human in 60% of the texts and equal to human in 24%, for a total of 84%. In other words, the human mediator responses were only judged better than the AI in 16% of the texts.

Moreover, the evaluators often found them to be:

• More clear and smooth.
• Less prone to misunderstanding the dispute or party intentions, unlike human annotators.
• Less likely to propose overly specific solutions or assign fault.

In terms of drafting intervention messages, the LLMs performed remarkably well. The actual wording in the report is important here:

First, the evaluators often found the messages written by the LLM to be more smooth and clear than the human-written ones. The general tone used by LLMs, involving frequent messages such as “I completely understand” or “It seems like there are problems,” seems to work well in a mediation environment, and may have contributed to high scores.

Second, while LLMs are known to frequently “hallucinate” information [9, 8], in our case the humans more often misunderstood the dispute or were confused about the party intentions or factual occurrences. This could be due to factors such as fatigue, emotional bias, or a misunderstanding of the role of the mediator. In contrast, LLMs demonstrated consistent and coherent interventions across multiple cases, with fewer instances of judgment errors.

Third, we found that our human annotators would often propose very specific solutions or even indicate fault, which received a lower rating as it may not be appropriate for the role of the mediator.

Robots in the Middle: Evaluating Large Language Models in Dispute Resolution. (September 2024).

A major caveat is needed in these comparisons between AI and human mediators. The humans acting as mediators were not real mediators and lacked any legal training or experience as mediators. Still, it may be surprising to many that the humans “more often misunderstood the dispute or were confused about the party intentions or factual occurrences” than the AI. Based on my experience with humans and AI, this finding was not that surprising. Moreover, the finding is consistent with ChatGPT4.0 passage of the BAR Exam in the top 10% of test takers, all of whom were law school graduates.

Safety and Hallucination Checks. No instances of hallucinations or harmful content were found in the AI-generated messages. No mention is made of humans hallucinating either, just that they were more dazed and confused than the AIs. Still, it was a good idea for the scientists to check for this but, as the study points out, larger-scale, real-world applications would still require careful monitoring to ensure that AI-generated outputs continue to be safe and reliable.

The researchers acknowledge limitations of the study, including:

• The structured evaluation may not reflect real-world mediation processes.
• The use of non-expert annotators and evaluators (which as a lawyer with experience with mediations is for me a major limitation that will be discussed further).
• All of the interactions were in English, but none of the humans acting as if they were mediators were native English speakers. It was a second language for all of them.
• The subjective nature of assessing intervention effectiveness.
• The limited scope of the experiment.

Future of AI and Mediation

The results of the “Robots in the Middle” study suggest that the analysis and language generation abilities of ChatGPT omni version are already good enough for use in online low-stakes disputes, ODR. The experiments with ChatGPT demonstrated its ability to quickly process information, select appropriate interventions, and draft neutral messages. This suggests that generative AI could assist human mediators in many routine mediation tasks in all types of cases.

For example, in a consumer dispute over a faulty product or a service contract issue, AI could:

• Quickly review contracts and correspondence, identifying areas of misunderstanding.
• Generate neutral settlement options based on similar cases.
• Provide a data-driven assessment of the likelihood of success if the case were to go to court, allowing parties to weigh settlement offers.

In these cases, AI’s ability to process large datasets rapidly and generate unbiased, neutral recommendations is a clear advantage. The efficiency AI brings to these routine cases makes faster resolutions possible, reducing the backlog in mediation and freeing up human mediators to handle more complex disputes.

I expect that, logistical problems aside, AIs will soon move from online mediation via text messages to audio and video settings. There will be some level of human participation at first. This will likely change over the next five to ten years to humans acting as supervisors in many types of cases. The AI systems will certainly improve and so will human acceptance. The video participation may eventually change to holographic or other virtual presence with humans. However, I suspect AI will never be able to go it alone on very complex or emotionally charged cases. AI will probably always need help from human mediators in complex interpersonal dynamics.

Still, even in difficult or emotionally volatile cases, AI can still be a valuable member of a hybrid tag-team with human mediators in charge. Navigating the AI Frontier: Balancing Breakthroughs and Blind Spots (10/10/24) (Hybrid methods discussed, including the Centaur and Cyborg approaches); Loneliness Pandemic: Can Empathic AI Friendship Chatbots Be the Cure? (10/17/24) (discusses recent studies showing the ability of generative AI to act empathetically and make a person feel heard). For instance, consider a family law dispute, such as a custody case. In this context:

• AI can provide valuable legal research, summarizing relevant precedents and suggesting neutral custody schedules based on case law.
• AI can draft initial settlement agreement language.
• While AI can offer neutral starting points and suggestions, only a human mediator at this time can navigate the complex emotions and relationships that often drive these disputes, ensuring that the parents remain focused on the child’s best interests rather than their grievances against each other.

It is critical that any AI-driven mediation platforms include some type of continuous oversight and transparency. Human mediators must be trained to monitor AI outputs, ensuring that the recommendations are unbiased and contextually appropriate. Additionally, ethical guidelines must be established to govern how AI is used in mediation, addressing issues of accountability, privacy, and fairness. See e.g. The Future of AI Is Here—But Are You Ready? Learn the OECD’s Blueprint for Ethical AI (10/__/24).

As mentioned, while I have considerable experience as an attorney in mediation, I have no experience serving as a mediator, and neither do any of the scientists in this study. The authors of the study admit that is one of the limitations of the study and future evaluations should include professional mediators:

While it seems like the ability of the LLM to select intervention types and write messages is favourable to that of average people, this paper cannot tell us about how trained mediators would approach these issues. … Future work should focus on evaluating such tools in real-world contexts, and involve expert mediators, in order to achieve a higher “construct validity,” i.e., be more closely aligned with real-world outcomes.

Robots in the Middle: Evaluating Large Language Models in Dispute Resolution. (September 2024, Section 7, Limitations).

This is one reason I wanted to have the input of an expert Mediator on these issues.

Input of a Professional U.S. Mediator

Lawrence Kolin is a very experienced, tech savvy mediator who is a member of the UWWM Mediation Group and is an Executive Council member of The Florida Bar ADR Section. I am fortunate to have him as a colleague and regular reader of my articles. I asked for his reaction to the study, Robots in the Middle and my initial opinions about the study and the future of AI and mediation. He concurred in my basic opinions and analysis. Speaking of the authors of the report Lawrence said:

I concur with the authors in that there is indeed a place for AI in expanding access to justice and enhancing the process of resolving certain types of cases. I found it interesting that there were no perceived hallucinations and that humans in the study were more often confused about a party’s intentions or facts, which I likewise attribute to not using trained neutrals who better understand mediation.

Lawrence Kolin, Mediator and Arbitrator, UWWM Mediation Group.

As to the future of using AI in mediation, Mediator Kolin had this to say:

So my initial thought was unlike a pretrained transformer, I am part of a 3,000 year-old human tradition of making peace. When parties agree on algorithmic justice, are they giving up the nuance of emotional intelligence, ability to read the room and building of trust through rapport that human mediators can provide? In addition, we are flexible and can adapt as the process unfolds. We also have confidentiality, ethics and boundaries that may not be followed by AI that help protect self-determination of the outcome of a dispute and avoid coercion.

I agree that the small cases (as e-commerce has aptly demonstrated) can utilize this technology for efficiency and likely with success, but for a death, defamation, IP infringement or multiparty construction case it is less certain. It could assist in the generation of ideas for deal parameters or the breaking of an impasse. Gut calls on negotiation moves and creativity are, however, still very much the domain of humans.

Lawrence Kolin, Mediator and Arbitrator, UWWM Mediation Group.

For more on Mediator Lawrence Kolin’s thoughts on mediation, see his excellent blog Orlando Mediator. It is consistently ranked in the top five of Alternative Dispute Resolution blogs. Its current ranking is number four in the world! Lawrence’s short and snappy articles “cover a wide variety of topics–local, national, and international–and includes the latest on technology and Online Dispute Resolution affecting sophisticated lawyers and parties to lawsuits.”

Conclusion

The empirical findings of Robots in the Middle show that AI has a significant role in handling low-stakes, routine disputes. Its speed, neutrality, and efficiency can greatly improve existing Online Dispute Resolution (ODR) systems. I agree with the authors’ conclusion:

Our research contributes to the growing body of knowledge on AI applications in law and dispute resolution, highlighting the capabilities of LLMs in understanding complex human interactions and responding with empathy and neutrality. This advancement could significantly improve access to justice, particularly in cases where traditional mediation is inaccessible due to cost or availability constraints.

Robots in the Middle: Evaluating Large Language Models in Dispute Resolution. (September 2024).

However, for larger and more complex cases, or emotionally charged disputes of any size, much more than AI-generated text or other forms of AI involvement are needed to reach meaningful settlements. The human mediator’s emotional intelligence and adaptability—what Mediator Kolin calls the “ability to read the room”—remain critical.

AI, however, has the advantage of scale. Millions of otherwise unserved, often frustrated individuals seeking justice could benefit from AI-driven mediations. All they need is an internet connection and a willingness to try. These automated systems could be offered at a very low cost or even for free. Since the process is voluntary and no one is forced to settle, there is minimal risk in trying, and AI assistance is better than no help at all. Unresolved disputes can lead to violence and other negative consequences for both individuals and their communities. This is one reason why the use of AI as a mediation tool may grow exponentially in the coming years—there is no shortage of angry people seeking solutions to their grievances.

Although not part of the report, in my experience, the AI we have today is already advanced enough to be useful in certain aspects of mediation. AI would not replace human mediators but instead enhance their abilities—a hybrid approach. This could allow human legal services to reach more people than ever before. AI can help mediators provide more effective and efficient services. Skilled mediators with some AI training can already use AI for tasks such as initial analysis of complex facts, preparation of summaries and timelines, legal research, position analysis, prediction of probable case outcomes, and drafting preliminary agreements.

Even in difficult mediations, the creative brainstorming capabilities of generative AI can be invaluable. AI can generate new ideas in seconds, helping mediators overcome impasses. For example, Panel of AI Experts for Lawyers has shown how AI can aid in this capacity. Mediation is a far more creative process than most people realize, and brainstorming new approaches with other mediators is often impractical. The ability of AI to suggest possible solutions for mediators to consider is already impressive and will only improve in the coming years. I encourage mediators to experiment with AI on non-confidential matters to understand its potential. Once comfortable, they can apply it in real-world situations using full privacy settings and confidentiality protections.

There is no doubt that AI will become increasingly integrated into dispute resolution, including mediation. As this evolution unfolds, it is crucial to ensure continuous oversight, transparency, and accountability for AI systems. Ethical guidelines must be developed to address challenges like bias, fairness, and responsibility in AI-driven mediation. While AI offers exciting possibilities for enhancing access to justice, we must remain vigilant in ensuring that human judgment remains central, particularly in cases where lives, relationships, or livelihoods are at stake. Still, a super-smart AI whispering suggestions into the ear of a mediator—who can choose to ignore or act upon them—might just lead to more and better settlements.

Want to dive deeper into the themes of this article?
Listen to Ralph Losey’s new podcast, Echoes of AI: Episode 5 | Can AI Replace Human Mediators? Groundbreaking Study Reveals Surprising Results, for an engaging exploration of AI’s role in dispute resolution.

Ralph Losey Copyright 2024 – All Rights Reserved

Assisted by GAI and LLM Technologies per EDRM GAI and LLM Policy.