AI tool helps people with opposing views find common ground

A chatbot-like tool powered by artificial intelligence (AI) can help people with differing views to find areas of agreement, an experiment with online discussion groups has shown.
The model, developed by Google DeepMind in London, was able to synthesize diverging opinions and produce summaries of each group’s position that took different perspectives into account. Participants preferred the AI-generated statements to ones written by human mediators, suggesting that such tools could be used to help support complex deliberations. The study was published in Science on 17 October1.
“You can see it as a sort of proof of concept that you can use AI, and, specifically, large language models, to fulfil part of the function that is fulfilled by current citizens’ assemblies and deliberative polls,” says Christopher Summerfield, a co-author of the study and research director at the UK AI Safety Institute. “People need to find common ground because collective action requires agreement.”
Democratic initiatives such as citizens’ assemblies, in which groups of people are asked to share their opinions on public-policy issues, help to ensure that politicians hear a wide variety of perspectives. But scaling up these initiatives can be tricky, and the discussions are typically restricted to relatively small groups to ensure that all voices are heard.
Intrigued by research into the potential of large language models (LLMs) to support such discussions, Summerfield and his colleagues came up with a study to assess whether AI could help people with opposing viewpoints to reach a compromise.
They used a fine-tuned version of the pretrained DeepMind LLM Chinchilla, and named their system the Habermas Machine, after the philosopher Jürgen Habermas, who developed a theory about how rational discussion can help to solve conflict.
In one of the experiments to test their model, the researchers recruited 439 UK residents and sorted them into smaller groups of 6 people. The group members discussed three questions related to UK public policy, sharing their personal opinions on each topic. These opinions were then fed to the AI, which generated overarching statements that combined all participants’ viewpoints. Participants were able to rank each statement and share critiques on them, and the AI incorporated these into a final summary of the group’s collective view.
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“The model is trained to try to produce a statement which will garner maximum endorsement by a group of people who have volunteered their opinions,” says Summerfield. “Because the model learns what your preferences are over these statements, it can then produce a statement which is most likely to satisfy everyone.”
Alongside the AI, one participant in each group was chosen to be a mediator, and each mediator was also told to produce a summary that best represented the views of all group members. Participants were shown both the AI’s and the mediator’s final summaries, and asked to rate them.
Most participants rated the summaries produced by the AI as better than those written by the mediator: 56% of participants preferred the AI’s work, compared with 44% who favoured the human-written statement. External reviewers were also asked to assess the summaries, and gave the AI-generated ones higher ratings for fairness, quality and clarity.
The research team then recruited a group of participants, chosen to be demographically representative of the UK population, for a virtual citizens’ assembly. In this scenario, group agreement on several contentious topics increased after interacting with the AI. This finding suggests that, if incorporated into a real citizens’ assembly, AI tools could make it easier for leaders to produce policy proposals that take different perspectives into account.
“The LLM could be used in many ways to assist in deliberations and serve roles previously reserved for human moderators,” says Ethan Busby at Brigham Young University in Provo, Utah, who studies how AI tools could improve society. “I think of this as the cutting edge of work in this space that has a big potential to address pressing social and political problems.” Summerfield adds that AI could even help to make conflict-resolution processes faster and more efficient.
“Actually applying these technologies into deliberative experiments and processes is really good to see,” says Sammy Mckinney, who studies deliberative democracy and its intersections with AI at the University of Cambridge, UK. But he adds that researchers should carefully consider the potential impacts of AI on the human aspect of debate. “A key reason to support citizen deliberation is that it creates certain kinds of spaces for people to relate to each other,” he says. “By removing more human contact and human facilitation, what are we losing?”
Summerfield acknowledges the limitations associated with AI technologies such as the Habermas Machine. “We did not train the model to try to intervene in the deliberation,” he says, which means that the model’s statements could include extremist or other problematic beliefs if participants expressed them. He adds that rigorous research into the impact AI has on society is crucial to understanding its value.
“Proceeding with caution seems important to me,” says Mckinney, “and then taking steps to, where possible, mitigate those concerns.”