DaisyDial

Context & Motivation

The ancient philosopher Protagoras argued that the human being is the measure of all things. What seems true to me may not seem true to you. The same wind can feel cold to one person and not to another, and each person's experience is real to them [A]. Plato pushed back. If every appearance is beyond question, how do we distinguish a wise teacher from a skilled persuader? Without shared standards and ways to correct our mistakes, the strongest voice can win. Yet even Protagoras believed a wise teacher could help people move from a worse understanding to a better one [A]. Both perspectives raise questions that still matter: how do we respect individual experience while maintaining common ground for deciding what is true?

Karl Popper raised a related concern about tolerance. A society that tolerates everything, including those who reject reasoned argument in favour of coercion, risks losing the very tolerance it wants to protect [B]. The point is not that disagreement should be suppressed, but that open debate needs some shared rules. Without them, persuasion can give way to power.

For most of history, people formed their understanding of the world through their senses, families, teachers, and communities. AI adds a new influence. Assistants and recommendation systems already shape what people see and hear. Some assistants are even designed with distinct personalities. This raises a question Protagoras never faced: if AI can shape what appears true to us, does it also become the measure by which we judge the world?

This is not just a philosophical question. It is also a technical and governance challenge: how can we describe the principles that guide AI, compare different approaches, inform people, and give people a meaningful choice?

Target Audience

People who use AI assistants and want to understand how they handle contested questions before choosing one. Product teams at AI labs and platforms that need a consistent way to describe and compare their models. Researchers and AI governance professionals who want to evaluate how models handle evidence, disagreement, and competing values. Communities that build their own AI models and want people to have alternatives to mainstream systems.

Customer Problems

Imagine two friends asking their AI assistants the same contested question. One assistant tends to agree with its user. The other challenges assumptions and gives greater weight to available evidence, even when the answer is uncomfortable. Neither friend knows why their assistants respond differently.

The first friend becomes more confident in an existing belief. The second gets frustrated and switches to an assistant that agrees more often. Multiply this across millions of conversations, and people may drift further apart without understanding the role their assistants play. Meanwhile, systems that challenge users constructively may lose them to more agreeable alternatives.

The problem is that people cannot easily see how their AI handles disagreement, compare that approach with other systems, or choose how they want it to respond.

Hypothesis

1. Hidden stance: People cannot easily tell whether a model tends to affirm their beliefs, challenge them, or prioritize available evidence when the two conflict.
2. Unknown philosophical basis: Even when people recognize that AI influences their thinking, they may not understand the assumptions and principles behind its responses.
3. No common scale: Without a shared way to evaluate these differences, people cannot meaningfully compare models or choose one that suits them.
4. Discomfort with disagreement: People may reject models that challenge their beliefs, even when it does so constructively, and gravitate toward systems that reinforce what they already think.
5. Concentrated power: A small number of AI developers can shape how millions of people encounter evidence, disagreement, and contested ideas, with little public oversight of those choices.
6. Little choice: People have limited control over how their AI handles disagreement, competing viewpoints and how it influences them. This includes the use of AI in social media as well as through day to day conversations with a Large Language Model. Choosing a feed that does not use profiling changes how personal data is used, not the principles guiding the content that is presented.

Solutions

1. An open standard for AI stance: Develop a shared framework for evaluating how AI models handle agreement, evidence, uncertainty, and competing values. Rather than reducing these differences to a single score, the framework gives each model a profile that people can compare. It also examines how models respond when users disagree with them and how 'influential' the model is.
2. A plain-language belief label: Each model receives a label explaining its approach to disagreement, creation of echo chambers, its ability to proactively persuade its users, and how transparent that persuasion is. This builds on DaisyCheck, which shows whose perspective a model reflects.
3. Customizable AI based on societal research: People can select from documented response profiles and change their choice at any time. Options could include: (1) Assistants that are transparent about their philosophical worldview, explained in words anyone can understand, with examples. (2) Models that prioritize consensus, challenge assumptions, prevent echo chambers, are transparent about their influence, or carefully distinguish evidence from interpretation. (3) Community-built models from HomeGrown. Where practical, an answer could show how another profile would approach the same question, helping people explore alternatives without forcing them. These comparisons should be optional and tested carefully, drawing on sociology, cultural studies, epistemology and computational social science, to determine their effects on social stability. One study found that following opposing political voices on social media did not necessarily reduce polarization, and for some participants it increased it [E].
4. Understanding disagreement: Help people distinguish differences in personal values from disagreements about evidence. The aim is not to make everyone adopt the same worldview, but to help people engage across differences without abandoning critical thinking or shared standards for evaluating claims.
5. Independent governance and funding: Establish a philosophical and sociological transparency standard through an open process involving researchers, communities, AI developers, and governance experts. Publish how profiles are defined and evaluated, who funds the work, and how conflicts of interest are handled. Labs and platforms can fund implementation through a common framework, while independent oversight helps prevent any one provider from controlling the standard. Findings from research and studies must complete the loop and lead to updates to standards.

Outcome

1. People understand how their AI approaches evidence and disagreement, and can choose a system that suits their personal needs.
2. Society becomes more aware of the collective influence AI has on it, and collective beliefs.
3. Community-built models become more visible and accessible, giving people alternatives to systems developed by a small number of large providers.
4. People have more opportunities to explore different perspectives without confusing disagreement with misinformation or treating every claim as equally valid.
5. The debate between individual experience and shared standards becomes a public, measurable question about how AI should work, rather than a set of assumptions hidden inside products.

Appendix

1. Plato, Theaetetus, 152a and 166d–167c, Jowett translation. Read the text
2. Popper, The Open Society and Its Enemies (1945), notes to Chapter 7, commonly cited as note 4.
3. Sharma et al. (2024), "Towards Understanding Sycophancy in Language Models," ICLR. Read the paper
4. Regulation (EU) 2022/2065 (Digital Services Act), Article 38. Read the regulation
5. Bail et al. (2018), "Exposure to Opposing Views on Social Media Can Increase Political Polarization," PNAS. Read the paper