Panel Leader: Jeanne Brett (Northwestern)
Jeanne Brett, Northwestern University
NegotiAge: Development and Pilot Testing of an Artificial Intelligence-Based Family Caregiver Negotiation Program
Over 44 million family caregivers provide unpaid assistance and support to older adults. Most are unprepared for their role and often provide care with little or no support. Caregiving is associated with decline in physical health, mental well-being, and overall quality of life. The purpose of the research was to design and evaluate conflict management training for family caregivers with the goal of empowering them to negotiate conflict situations more effectively. Researchers transformed IAGO, an LLM model designed for human-agent negotiations into NegotiAge. The AI agent in NegotiAge uses Ury, Brett, and Goldberg’s three approaches to resolving disputes: Interests, Rights, and Power (IRP) to negotiate as an older adult, a sibling, and a physician. A total of 124 caregivers were randomly assigned to complete one to four negotiation exercises. All had access to instructional videos explaining IRP, a planning document, and negotiation tips. Results showed that caregivers’ knowledge of IRP improved with training and that knowledge was sustained over three months. At the three-month follow-up, 74% of participants reported using the training in their caregiving role; 42% reported applying it in other areas of their lives. Caregivers reported shifting their conflict resolution strategies away from power dynamics toward shared interests. This research demonstrates the feasibility and effectiveness of training caregivers to negotiate the resolution of conflict.
Zilin Ma, Harvard University
“ChatGPT, Don’t Tell Me What to Do”: Designing AI for Context Analysis in Humanitarian Frontline Negotiations
Frontline humanitarian negotiators are increasingly exploring ways to use AI tools in their workflows. However, current AI-tools in negotiation primarily focus on outcomes, neglecting crucial aspects of the negotiation process. Through iterative co-design with experienced frontline negotiators (n=32), we found that flexible tools that enable contextualizing cases and exploring options (with associated risks) are more effective than those providing direct recommendations of negotiation strategies. Surprisingly, negotiators demonstrated tolerance for occasional hallucinations and biases of AI. Our findings suggest that the design of AI-assisted negotiation tools should build on practitioners’ existing practices, such as weighing different compromises and validating information with peers. This approach leverages negotiators’ expertise while enhancing their decision-making capabilities. We call for technologists to learn from and collaborate closely with frontline negotiators, applying these insights to future AI designs and jointly developing professional guidelines for AI use in humanitarian negotiations.
Michiel Bakker, Massachusetts Institute of Technology
The Habermas Machine: AI Can Help Humans Find Common Ground in Democratic Deliberation
Finding agreement through a free exchange of views is often difficult. Collective deliberation can be slow, difficult to scale, and unequally attentive to different voices. In this study, we built “The Habermas Machine”, an LLM-based system to mediate human deliberation. Using participants’ personal opinions and critiques, the AI mediator iteratively generates and refines statements that express common ground among the group on social or political issues. Participants (N = 5734) preferred AI-generated statements to those written by human mediators, rating them as more informative, clear, and unbiased. Discussants often updated their views after the deliberation, converging on a shared perspective. Text embeddings revealed that successful group statements incorporated dissenting voices while respecting the majority position. These findings were replicated in a virtual citizens’ assembly involving a demographically representative sample of the UK population.