Remote Conflict Resolution

Code that calms a conflict.
 Where politics stalls, protocols begin. When talks fail, these women deploy algorithms.
 Their tools don’t shout; they translate, simulate, and resolve.
 This week, diplomacy runs on data. 📌 Heather Murray The diplomat who taught AI fluency to foreign envoys. Trained 1K+ negotiators on agentic AI in conflict talks. Built frameworks so technologists and states can align. AI is now part of the language of peace. 📌 Caroline Gorski The strategist who war-gamed AI for real-world peace. Tested AI governance under NATO defense conditions. Former Rolls-Royce exec, now builds trust protocols. Stability, coded before the conflict starts. 📌 Oriana Medlicott The ethicist who mapped the EU’s AI red lines. Helped write compliance into cross-border tech law. Guides firms through regulation before rollout hits. Built the rules trust can live inside. 📌 Megi Kurdadze The mediator who trains AI to de-escalate. Equips peacebuilders with real-time scenario models. Codes simulations used in global ceasefire talks. Built tools that see past human deadlock. 📌 Alice Walton The coder who gave backchannels a second voice. Built multilingual AI for crisis-time sentiment scans. Her tools catch tension before it breaks the room. Reads what diplomacy won’t say aloud. 📌 Sharona Hutton The strategist who coded carbon into compliance. Models carbon credit swaps for treaty alignment. Her tools simplify climate math for negotiators. Brokers emission cuts before talks collapse. 📌 Toju Duke The coder who translated ethics into EU law. Built mediation bots compliant with the AI Act. Piloted Brexit dispute AI across UK trade lines. Trust coded between rivals. 📌 Maria Kolomychenko The coder behind peace-time pattern detection. Built sentiment AI for Ukraine crisis diplomacy. Mapped tension shifts before human translators could. Backchannel calm, delivered in real-time. 📌 Rhea Mohan The coder negotiating water before it runs dry. Built AI models for Indus basin treaty scenarios. Mapped flow splits across borders and crises. Brought rivers to the table before the floods did. 📌 Liz Parrish The diplomat modeling climate through code. Simulates Arctic resource splits with AI equity tools. Her forecasts guide multilateral treaty talks. Mapped compromise before conflict could harden. 📌 Nivedita Arora The strategist rerouting supply in crisis. Built Maersk’s AI to dodge Red Sea disruptions. Her models resolve US–China tariff gridlocks. Kept trade flowing while borders froze. 📌 Alondra Nelson The strategist who gave AI a rights blueprint. Drafted policy that bridged tech and civil law. Chaired the U.S. AI Bill of Rights at global level. Wrote protections before the systems scaled. They didn’t just join the table,
 they rewrote the protocol stack beneath it.
 AI stepped in where humans froze. Which code built the strongest truce?

📢 Thrilled to share that my latest article has just been published in the CIArb Journal – Arbitration: The International Journal of Arbitration, Mediation and Dispute Management, Volume 91, Issue 2 (2025). Titled “AI Meets Mediation: Shaping the Future of Dispute Resolution in a Digital World,” the piece explores how AI is transforming the landscape of commercial mediation through tools like predictive analytics, virtual platforms, and automated document review. While these technologies bring unprecedented efficiency and cost-effectiveness, they also raise critical ethical concerns — from algorithmic bias to the risks of eroding the human touch at the heart of mediation. Drawing on practical examples, I reflect on the impact of AI on time, cost, and outcome quality — and on how we must balance innovation with responsibility to build trust in AI-assisted dispute resolution. You can read the full article here via Kluwer Law Online:  https://lnkd.in/db5zJUxs Thank you to the editorial board and CIArb for the opportunity to contribute to such a timely edition. #InternationalArbitration #Mediation #AIinLaw #DigitalJustice #CIArb #KluwerArbitration #LegalTech #DisputeResolution

Can AI beat humans in a mediation? I’ve been judging the IBMC International Business Mediation Competition this weekend. I undertook an experiment, uploading one of the competition scenarios given to the students to ChatGPT and asking it “What suggestions do you have to resolve this dispute?” After the session ended I compared ChatGPT’s results to what happened at mediation.  The AI proposed a settlement was strikingly similar to those what the two highly skilled and trained international teams arrived at. At first glance, this appeared to demonstrate AI’s growing sophistication in handling complex negotiation dynamics. It analysed the facts, balanced commercial interests, and produced a reasonable, rational settlement plan including the payment of compensation. But a closer look revealed that the AI-generated proposal failed to address a critical legal implication or consequence of the preferred solution: by accepting the alternative cooperation (“expanding the pie”) model offered by one of the parties, AI missed that the other party would have eliminated its loss and thus its claim for damages. The proposed “solution” would have extinguished the very basis for any compensation claim. A decisive point that AI had not detected. This illustrates a key limitation of AI in mediation contexts. AI can organise facts, generate creative settlement options, and simulate reasoned decision-making, but it does not yet understand the legal consequences or strategic trade-offs that good and experienced professionals instinctively consider. Equally significant was the absence of any recognition of the human dimensions underpinning the conflict: such as the sense of betrayal after years of partnership, the fear of financial instability, the reputational pressures, and the unspoken personal stakes behind each business decision. AI’s analysis was logical. Mediation, however, is rarely just logical. Rather than viewing AI as a threat, mediators might see it as a tool to assist and speed up the process and maintains the place of humans in idea creation, critical analysis and a deeper understanding of the human and psychological aspects of conflict. AI cannot read between the lines or identify the unsaid. It cannot rebuild trust, reframe identity, or help people recover dignity. AI processes facts. Mediators process relationships. AI is a threat only to mediators who see their role as mechanical, those who simply shuttle numbers and offers. But for mediators who see their craft as human-centred, creative and problem solving, AI is an opportunity. AI can reduce administrative load, summarise facts, generate scenario modelling and provide factual clarity quickly and efficiently. Oh... and then ChatGPT offered to draft the settlement agreement in the form of a Tomlin Order to be submitted. In less than 30 seconds, it produced one barely indistinguishable from those in practise. #ibmc2025 #mediation #negotiation #conflictresolution #disputeresolution #adr

A good team is not one without disagreement. It is one that can surface disagreement early enough to act on it. But that’s where things get tricky. The moves that drive real progress (e.g., naming a tension, clarifying a conflict, or forcing a tradeoff) can feel like setbacks in the moment. They interrupt the flow. They can even make consensus temporarily worse. This new paper from my team starts from that reality and asks what it would mean for AI to help groups navigate the uncomfortable path to alignment. 📖 ProMediate: A Socio-cognitive framework for evaluating proactive agents in multi-party negotiation (https://lnkd.in/gBFiEBU9), by Ziyi Liu, Bahareh Sarrafzadeh, Pei Zhou, Jieyu Zhao, and Ashish Sharma. The paper treats AI less like a participant in a group conversation, and more like a mediator that watches the interaction and decides both when to intervene and how. The authors make this concrete with a simulation testbed and a set of metrics that track consensus as it evolves over time, measuring not just where groups end up, but how agreement shifts after each intervention. What emerges is that the act of intervening is surprisingly delicate. The moments that most improve outcomes often do not feel helpful in the moment. Surfacing a missing perspective or naming a tension can lower consensus before it improves it, interrupting the flow so teams can move past polite misalignment toward something more real. Timing matters, and so does the intervention strategy. Save this for your “collaboration is more than consensus” shelf. #BeyondTheAbstract #AIAtWork #Collaboration #OAR #AppliedResearch

I’m excited about the AI tools emerging around early case valuation, estimating likely settlement ranges well before a case reaches a courtroom. Popular LLMs and AI tools are already being used by clients, lawyers, and self-represented parties to assess exposure, damages, and “what a case is worth,” as they prepare for mediation. Recently published research and testing offer good reminders of current limitations. Even under controlled conditions, identical case inputs can produce meaningfully different legal conclusions from the same AI system. Same facts, same framing; different weighting of risk, exposure, and “reasonable” outcomes. For those using these tools, here are a few ways to make them more useful: – Decompose the task. Look at liability, damages, and risk separately instead of collapsing everything into one long prompt. – Run the query multiple times so you can spot inconsistencies and you don’t get wedded to the most persuasive-sounding answer. – Keep humans central. Professional judgment is the safeguard against low-quality analysis and fabricated responses that sound confident. AI may help inform valuation in a case. But like every tool in mediation, it works best when it supports the participants’ own careful thinking. I'm curious what others are seeing in practice. See: Same Question, Different Answers: What 12,000 Tests Reveal About AI and Legal Notice, National Law Review

𝗔𝗜 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗯𝗲𝗹𝗼𝗻𝗴 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗺𝗲𝗱𝗶𝗮𝘁𝗶𝗼𝗻 𝗿𝗼𝗼𝗺. Everywhere else, it can change everything for mediators. I say this as an internationally accredited mediator and co-founder of Musaliha International Center for Arbitration & Dispute Resolution (MICADR), and as someone who has spent the last two years at EnablifyAI building AI systems for law firms and training over 800 legal professionals across Pakistan and East Africa. Here is where AI can genuinely strengthen a mediation centre's operations, always with explicit party consent, always outside the room itself. 𝗕𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝗺𝗲𝗱𝗶𝗮𝘁𝗶𝗼𝗻 𝗯𝗲𝗴𝗶𝗻𝘀  • Matching case to mediator profile based on case type, complexity, and communication style fit, rather than institutional memory and availability alone  • Processing party-submitted case briefs to identify points of contention, common facts, and existing areas of agreement before the first joint session  • Running that case intelligence through legal databases to surface relevant precedent, useful for mediators and legal counsel during reality testing  • Multilingual intake and triage across languages, standardizing how cases are logged and routed None of this replaces the mediator's read of the room. It gives them a structured starting map instead of walking in blind. 𝗗𝘂𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗯𝗿𝗲𝗮𝗸𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘀𝗲𝘀𝘀𝗶𝗼𝗻𝘀 This is the part people misunderstand most. The core value a mediator brings, reading emotion, sensing what is unsaid, building the trust that gets someone to reveal their actual interest, cannot be mediated through a machine. But while a mediator steps out to think, there is value in a system trained specifically on mediation strategy that helps think through how a party might respond to a question, or supports managing a genuine impasse, functioning as a private preparation tool consulted alone. 𝗔𝗳𝘁𝗲𝗿 𝘁𝗵𝗲 𝗺𝗲𝗱𝗶𝗮𝘁𝗶𝗼𝗻 Refining first-draft settlement agreements prepared by legal counsel, flagging ambiguous terms before the document returns to the parties Confidentiality is the foundation the profession is built on. Parties must be told explicitly and give informed consent and AI tools with default training settings have no place here. Deployment needs to be on-premises where sensitivity demands it, or enterprise-grade with contractually guaranteed zero data retention. 𝗪𝗵𝘆 𝗜 𝗮𝗺 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 Mediation centres will be approached by vendors who do not understand mediation at all. At EnablifyAI, we build these systems trained on a centre's own data and methodology, deployed on infrastructure matching the sensitivity of what you handle. Mediation is not a side project for me. It is where I started. If you are a mediation centre or mediator thinking seriously about where AI fits, and where it absolutely should not, I would welcome the conversation. Reach out via LinkedIn or the email address in the comments.

"Technology is changing not only the way we communicate; it is altering the way we disagree and the way we resolve our disputes. And it is generating new kinds of disputes, many of which grow out of all the new capabilities we enjoy. Technology is also changing people’s expectations about how disputes should be resolved. People now believe that they should be able to report a problem at any time of day and get quick, round-the-clock support to resolve it transparently and effectively." This excerpt is from Colin Rule's article "Technology and the Future of Dispute Resolution" (see the resource link in the comment section). According to Rule, Online Dispute Resolution (ODR) is arguably the future of alternative dispute resolution. ODR refers to the application of technology in resolving disputes. One key advantage of ODR is that, unlike the traditional legal system, it is not constrained by geographical location or jurisdiction. Transactions between parties in different states or continents often face jurisdictional challenges, high costs, and limited access to justice. However, ODR systems are designed to address these issues, providing accessible solutions globally, regardless of geographic boundaries. Additionally, ODR systems can be embedded into software that delivers fast and fair redress to parties. These systems have influenced conventional legal procedures, as many parties now prefer to communicate their dispute, have it evaluated, and receive a resolution within a few hours — a stark contrast to the lengthy processes of diagnosis, negotiation, mediation, arbitration, and ombuds services (terms that some parties may struggle to understand). However, while ODR is an excellent tool for dispute resolution, it may not be suitable for every situation. Some parties may resist technology-based solutions, and ODR systems may struggle to interpret nuanced emotional communications. Therefore, it is essential for legal professionals to understand when to leverage technology in dispute resolution and when traditional methods may be more appropriate. Wishing you a great day! 🌻 #projectlawyer #disputeresolution #law #technology #techlaw