Large Language Models (LLMs) are increasingly deployed in socially complex, interaction-driven tasks, yet their ability to mirror human behavior in emotionally and strategically complex contexts remains underexplored. This study assesses the behavioral alignment of personality-prompted LLMs in adversarial dispute resolution by simulating multi-turn conflict dialogues that incorporate negotiation. Each LLM is guided by a matched Five-Factor personality profile to control for individual variation and enhance realism. We evaluate alignment across three dimensions: linguistic style, emotional expression (e.g., anger dynamics), and strategic behavior. GPT-4.1 achieves the closest alignment with humans in linguistic style and emotional dynamics, while Claude-3.7-Sonnet best reflects strategic behavior. Nonetheless, substantial alignment gaps persist. Our findings establish a benchmark for alignment between LLMs and humans in socially complex interactions, underscoring both the promise and the limitations of personality conditioning in dialogue modeling.

--In conflict, people use emotional expressions to shape their counterparts' thoughts, feelings, and actions. This paper explores whether automatic text emotion recognition offers insight into this influence in the context of dispute resolution. Prior work has shown the promise of such methods in negotiations; however, disputes evoke stronger emotions and different social processes. We use a large corpus of buyer-seller dispute dialogues to investigate how emotional expressions shape subjective and objective outcomes. We further demonstrate that large-language models yield considerably greater explanatory power than previous methods for emotion intensity annotation and better match the decisions of human annotators. Findings support existing theoretical models for how emotional expressions contribute to conflict escalation and resolution and suggest that agent-based systems could be useful in managing disputes by recognizing and potentially mitigating emotional escalation. Emotional expressions serve essential social functions in human relationships. They convey one's beliefs, desires, and intentions -- shaping the beliefs, desires, and intentions of interaction partners [1], [2]. People high in emotional intelligence achieve more success in navigating emotional relationships [3], and there exists growing interest in creating AI agents that understand and enact these social functions [4], [5]. Prior work suggests that emotionally-aware agents are suitable for a growing list of applications, including teaching people to convey emotions effectively [6], improving human-agent interaction [7], detecting and moderating toxic communication [8], and serving as methodological tools for studying human emotion [9]. This paper examines the capacity of agents to understand human emotional expressions in the context of text-based dispute resolution. Disputes arise when one party in a relationship (an individual, group, or nation) levies a claim that another party refuses to accept, thus threatening the future of the relationship [10].

We present KODIS, a dyadic dispute resolution corpus containing thousands of dialogues from over 75 countries. Motivated by a theoretical model of culture and conflict, participants engage in a typical customer service dispute designed by experts to evoke strong emotions and conflict. The corpus contains a rich set of dispositional, process, and outcome measures. The initial analysis supports theories of how anger expressions lead to escalatory spirals and highlights cultural differences in emotional expression. We make this corpus and data collection framework available to the community.

Mediation is a dispute resolution method featuring a neutral third-party (mediator) who intervenes to help the individuals resolve their dispute. In this paper, we investigate to which extent large language models (LLMs) are able to act as mediators. We investigate whether LLMs are able to analyze dispute conversations, select suitable intervention types, and generate appropriate intervention messages. Using a novel, manually created dataset of 50 dispute scenarios, we conduct a blind evaluation comparing LLMs with human annotators across several key metrics. Overall, the LLMs showed strong performance, even outperforming our human annotators across dimensions. Specifically, in 62% of the cases, the LLMs chose intervention types that were rated as better than or equivalent to those chosen by humans. Moreover, in 84% of the cases, the intervention messages generated by the LLMs were rated as better than or equal to the intervention messages written by humans. LLMs likewise performed favourably on metrics such as impartiality, understanding and contextualization. Our results demonstrate the potential of integrating AI in online dispute resolution (ODR) platforms.

Since the introduction of Generative AI (GenAI) in 2022, its ability to simulate human intelligence and generate content has sparked both enthusiasm and concern. While much of the criticism focuses on AI's potential to perpetuate bias, create emotional dissonance, displace jobs, and raise ethical questions, these concerns often overlook the practical benefits of AI, particularly in legal contexts. This article examines the integration of AI into arbitration, arguing that the Federal Arbitration Act (FAA) allows parties to contractually choose AI-driven arbitration, despite traditional reservations. The article makes three key contributions: (1) It shifts the focus from debates over AI's personhood to the practical aspects of incorporating AI into arbitration, asserting that AI can effectively serve as an arbitrator if both parties agree; (2) It positions arbitration as an ideal starting point for broader AI adoption in the legal field, given its flexibility and the autonomy it grants parties to define their standards of fairness; and (3) It outlines future research directions, emphasizing the importance of empirically comparing AI and human arbitration, which could lead to the development of distinct systems. By advocating for the use of AI in arbitration, this article underscores the importance of respecting contractual autonomy and creating an environment that allows AI's potential to be fully realized. Drawing on the insights of Judge Richard Posner, the article argues that the ethical obligations of AI in arbitration should be understood within the context of its technological strengths and the voluntary nature of arbitration agreements. Ultimately, it calls for a balanced, open-minded approach to AI in arbitration, recognizing its potential to enhance the efficiency, fairness, and flexibility of dispute resolution.

In this article, we introduce LLMediator, an experimental platform designed to enhance online dispute resolution (ODR) by utilizing capabilities of state-of-the-art large language models (LLMs) such as GPT-4. In the context of high-volume, low-intensity legal disputes, alternative dispute resolution methods such as negotiation and mediation offer accessible and cooperative solutions for laypeople. These approaches can be carried out online on ODR platforms. LLMediator aims to improve the efficacy of such processes by leveraging GPT-4 to reformulate user messages, draft mediator responses, and potentially autonomously engage in the discussions. We present and discuss several features of LLMediator and conduct initial qualitative evaluations, demonstrating the potential for LLMs to support ODR and facilitate amicable settlements. The initial proof of concept is promising and opens up avenues for further research in AI-assisted negotiation and mediation.

E-Commerce marketplaces support millions of daily transactions, and some disagreements between buyers and sellers are unavoidable. Resolving disputes in an accurate, fast, and fair manner is of great importance for maintaining a trustworthy platform. Simple cases can be automated, but intricate cases are not sufficiently addressed by hard-coded rules, and therefore most disputes are currently resolved by people. In this work we take a first step towards automatically assisting human agents in dispute resolution at scale. We construct a large dataset of disputes from the eBay online marketplace, and identify several interesting behavioral and linguistic patterns. We then train classifiers to predict dispute outcomes with high accuracy. We explore the model and the dataset, reporting interesting correlations, important features, and insights.

On May 13, 2021, the London Disputes Week conference ("LIDW21") hosted a panel of thought leaders to discuss the intersection of artificial intelligence ("AI") and dispute resolution ("DR"). The panel was moderated by Dan Wyatt of RPC and featured Charles Morgan, national co-leader of McCarthy Tétrault's Cyber/Data Group, Trish Shaw of Beyond Reach Consulting, Sophia Adams Bhatti of Simmons Wavelength Limited and Steve Shinn of Disputed.iou. The panel was part of the session entitled, "The use of technology and AI in the future of dispute resolution in London." This article summarizes some of the key points raised during this session. LIDW21 is an international conference with a focus on centering London, England as the global centre for dispute resolution.

Of all the digital technologies that are driving change in businesses, Artificial Intelligence (AI) is perhaps the most disruptive of all and has taken over the globe by storm. Currently, AI based technology solutions are being deployed in manufacturing, automotive, e-commerce, construction, smart cities and warehousing. However, within the legal and financial sectors, the implementation of AI is not as rapid. With a fast-changing environment, adaptation of new emerging technologies and increasing volumes of information, we will have to accept that AI will be taking over significant aspects of jobs in the future. According to a recent study by the International Data Corporation, worldwide data is expected to grow 61 per cent to 175 zettabytes in five years.--Financial

Time is ripe for legislation containing compulsory'pre-litigation mediation': CJI NEW DELHI: Chief Justice of India S A Bobde on Saturday said the time is ripe to devise a comprehensive legislation which contains "compulsory pre-litigation mediation" that would ensure efficiency and reduce the time of pendency for parties as well as courts. Speaking at the 3rd edition of of an international conference on'Arbitration in the Era of Globalisation', Justice Bobde said a robust "arbitration bar" is critical to the development of institutional arbitration in India as it would ensure availability and accessibility of practitioners with knowledge and experience. Justice Bobde said that today arbitration plays an essential role in the global infrastructure of international trade, commerce and investment and as an integral member of the global community and a trading and investment giant, how India engages with international arbitration has important ramifications on international trans-boundary flows of trade, commerce and investments as a whole. "The pre-institution mediation and settlement as mentioned in the Commercial Courts Act would pave the way for many more institutions to emphasize on the need of pre-litigation mediation considering its very many benefits. "I think the time is ripe to devise a comprehensive legislation which contains compulsory pre-litigation mediation and a remedy for the biggest drawback in a mediation agreement that is to say the unenforceability of an agreement arrived at a mediation would ensure efficiency and also reduce the time pendency for parties as well as the courts," he said. While talking about India's role in international arbitration, Justice Bobde said, "In recent times, globalisation has led to the dramatic growth in cross-border transactions involving India, which has led to an increasing demand for cross-border arbitration.