Why ‘Strong Opinions, Weakly Held’ isn't as great a thinking tool as you might think.
It’s Not Enough to Be Right. You Also Have to Be Kind.
Takedowns and clever quips are easy, but empathy and persuasion are better
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Independent, data-driven analysis of politics, public opinion polls, and elections. From author, journalist, and pollster G. Elliott Morris. Click to read Strength In Numbers, by G. Elliott Morris, a Substack publication with tens of thousands of subscribers.
Subscribe to Strength In Numbers
Independent, data-driven analysis of politics, public opinion polls, and elections. From author, journalist, and pollster G. Elliott Morris. Click to read Strength In Numbers, by G. Elliott Morris, a Substack publication with tens of thousands of subscribers.
Weighted DVF: A Simple Model for Scoring and Prioritizing Ideas
Discover a powerful yet straightforward approach to evaluating new ideas by balancing desirability, viability, and feasibility. Learn how…
The thinking style that makes people vulnerable to extremism | Psyche Ideas
The people most drawn to ideological thinking and authoritarianism aren’t aware of what makes them so susceptible
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 trained an artificial intelligence (AI) 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. , Editor’s summary To act collectively, groups must reach agreement; however, this can be challenging when discussants present very different but valid opinions. Tessler et al . investigated whether artificial intelligence (AI) can help groups reach a consensus during democratic debate (see the Policy Forum by Nyhan and Titiunik). The authors trained a large language model called the Habermas Machine to serve as an AI mediator that helped small UK groups find common ground while discussing divisive political issues such as Brexit, immigration, the minimum wage, climate change, and universal childcare. Compared with human mediators, AI mediators produced more palatable statements that generated wide agreement and left groups less divided. The AI’s statements were more clear, logical, and informative without alienating minority perspectives. This work carries policy implications for AI’s potential to unify deeply divided groups. —Ekeoma Uzogara , INTRODUCTION Democracy, at its best, rests upon the free and equal exchange of views among people with diverse perspectives. Collective deliberation can be effectively supported by structured events, such as citizens’ assemblies, but such events are expensive, are difficult to scale, and can result in voices being heard unequally. This study investigates the potential of artificial intelligence (AI) to overcome these limitations, using AI mediation to help people find common ground on complex social and political issues. RATIONALE We asked whether an AI system based on large language models (LLMs) could successfully capture the underlying shared perspectives of a group of human discussants by writing a “group statement” that the discussants would collectively endorse. Inspired by Jürgen Habermas’s theory of communicative action, we designed the “Habermas Machine” to iteratively generate group statements that were based on the personal opinions and critiques from individual users, with the goal of maximizing group approval ratings. Through successive rounds of human data collection, we used supervised fine-tuning and reward modeling to progressively enhance the Habermas Machine’s ability to capture shared perspectives. To evaluate the efficacy of AI-mediated deliberation, we conducted a series of experiments with over 5000 participants from the United Kingdom. These experiments investigated the impact of AI mediation on finding common ground, how the views of discussants changed across the process, the balance between minority and majority perspectives in group statements, and potential biases present in those statements. Lastly, we used the Habermas Machine for a virtual citizens’ assembly, assessing its ability to support deliberation on controversial issues within a demographically representative sample of UK residents. RESULTS Group opinion statements generated by the Habermas Machine were consistently preferred by group members over those written by human mediators and received higher ratings from external judges for quality, clarity, informativeness, and perceived fairness. AI-mediated deliberation also reduced division within groups, with participants’ reported stances converging toward a common position on the issue after deliberation; this result did not occur when discussants directly exchanged views, unmediated. Although support for the majority position increased after deliberation, the Habermas Machine demonstrably incorporated minority critiques into revised statements. We replicated these results in a virtual citizens’ assembly, additionally finding that during AI-mediated deliberation, the views of groups of discussants tended to move in a similar direction on controversial issues. These shifts were not attributable to biases in the AI, suggesting that the deliberation process genuinely aided the emergence of shared perspectives on potentially polarizing social and political issues. CONCLUSION This research demonstrates the potential of AI to enhance collective deliberation by finding common ground among discussants with diverse views. The AI-mediated approach is time-efficient, fair, scalable, and outperforms human mediators on key dimensions. Rather than simply appealing to the majority, the Habermas Machine prominently incorporated dissenting voices into the group statements. AI-assisted deliberation is not without its risks, however; to ensure fair and inclusive debate, steps must be taken to ensure users are representative of the target population and are prepared to contribute in good faith. Under such conditions, AI may be leveraged to improve collective decision-making across various domains, from contract negotiations and conflict resolution to political discussions and citizens’ assemblies. The Habermas Machine offers a promising tool for finding agreement and promoting collective action in an increasingly divided world. AI helps people find common ground in collective deliberation. (Left) The AI mediator uses participants’ opinions to generate group statements and iteratively refines those statements through participants’ critiques. (Middle) Statements from the AI mediator (purple) garner stronger endorsement than those written by a human mediator (orange). (Right) AI mediation leaves groups less divided after deliberation, whereas simply sharing opinions with others does not.
Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models
Much recent work seeks to evaluate values and opinions in large language models (LLMs) using multiple-choice surveys and questionnaires. Most of this work is motivated by concerns around real-world LLM applications. For example, politically-biased LLMs may subtly influence society when they are used by millions of people. Such real-world concerns, however, stand in stark contrast to the artificiality of current evaluations: real users do not typically ask LLMs survey questions. Motivated by this discrepancy, we challenge the prevailing constrained evaluation paradigm for values and opinions in LLMs and explore more realistic unconstrained evaluations. As a case study, we focus on the popular Political Compass Test (PCT). In a systematic review, we find that most prior work using the PCT *forces models to comply with the PCT's multiple-choice format. We show that models give substantively different answers when not forced; that answers change depending on how models are forced; and that answers lack paraphrase robustness. Then, we demonstrate that models give different answers yet again in a more realistic open-ended answer setting. We distill these findings into recommendations and open challenges in evaluating values and opinions in LLMs.
Why opinion on AI is so divided
AI power users are pulling away from everyone else.
An Experimental Method to Study Opinion Diffusion in Human-AI Hybrid Societies
As artificial intelligence increasingly mediates public discourse, it becomes important to understand how human-AI collectives shape opinion formation, deliberation, and democratic outcomes. We present a novel experimental method for studying opinion dynamics in hybrid human-AI social networks. Participants, human or AI, were embedded in $5\times5$ grid lattice networks and iteratively asked to select and revise statements on a given polarizing topic over eight rounds. We compared three conditions: human-only, AI-only, and hybrid networks with equal proportions of human and AI participants. Hybrid human-AI networks achieved the lowest final polarization while, in contrast, human-only networks exhibited higher polarization with lower neighbor agreement. We also ran additional experiments varying Large Language Model (LLM) prompt framing to explore whether instruction design might influence convergence patterns. Although these early findings are preliminary and cannot yet support broad generalizations, they highlight the potential value of experimental social networks for understanding opinion dynamics in human-AI hybrid societies.
Michael Baym on Twitter / X
Remember that you, by definition, cannot distinguish between a good idea and a failure of your own knowledge, judgement, or taste. This is why external (ideally expert) feedback is crucial— Michael Baym (@baym) March 25, 2026
Social media is populist and polarising; AI may be the opposite
Large language models elevate expert consensus and moderate views, in sharp contrast to social platforms
How To Think Real Good | Meta-rationality
Practical epistemology: heuristics for how to think about difficult things, particularly using formal methods.
How latent and prompting biases in AI-generated historical narratives influence opinions
Abstract. Large language models (LLMs) can be used to persuade people on a range of issues, particularly through user-driven strategies such as personalizi
Don't soften feedback.
When I talk about balancing being directive and empowering as a leader, the distinction between being kind and being nice often comes up.
