From shared evidence to better democracy

1. Make evidence easier to inspect

AI agents gather sourced statements to help people understand the landscape around a proposal without separately reconstructing hundreds of sources. Citizens, creators, researchers, journalists, and decision-makers can inspect the same record. Keeping positions attributable makes it possible to check their context, challenge an interpretation, and contribute missing evidence.

Provenance is a starting point, not a guarantee of truth. A quote can be authentic while its claim is wrong. Coverage can also be incomplete or skewed. Scrutiny must extend to the selection of sources and the way proposals are framed.

2. Make disagreement more informative

A single summary can hide important differences. People might disagree about the probability of a dangerous capability, agree about the danger but disagree about an intervention, or reason from different empirical assumptions and values.

Participation gives people ways to respond to the record. Opinions, votes, delegations, and changes of position can provide clues about reasoning. They do not reveal reasoning on their own: a changed vote might reflect new evidence, social influence, or a different interpretation of the question.

Delegation when nobody can evaluate everything

AI governance spans machine learning, cybersecurity, biology, law, economics, and international relations. YouCongress already lets people delegate to trusted people while retaining the ability to vote directly. Today those delegations apply across issues.

A future version could make trust specific to a domain and connect it more explicitly to evidence: whom do you rely on, for which questions, and why do they support an intervention? That is a direction to investigate, not an existing domain-specific delegation feature.

Help creators understand their audiences

Creators can use Reconsider to pair an article or video with relevant proposals and ask readers or viewers where they stood before and where they stand now. The results show reported changes in views and which featured people participants chose as delegates. That feedback could help creators identify disagreements and questions to explore next.

These responses come from people who choose to participate. They do not represent the whole audience or establish that the content caused a change in opinion.

Help new trusted voices emerge

Sharing thoughtful arguments, responding to objections, and earning others’ trust through delegation could help contributors become known beyond their existing circles. We hope this creates opportunities for people who have never considered running for office to discover that others value their judgment. Public service is one possible outcome; contributing to better debate is valuable in itself.

This is a hypothesis to test. Gaining delegates can indicate trust, but does not by itself establish sound judgment or readiness for office. We need to learn whether participation helps overlooked voices earn attention and whether that trust is well placed.

3. Help decision-makers turn understanding into action

Decision-makers in government, public institutions, and other organizations could use YouCongress to compare sourced positions, examine objections, and understand what participants support and why. We aim to help them assess proposals alongside the perspectives of people affected by their choices. Participation can inform a decision, while decision-makers remain responsible for seeking missing perspectives and judging the evidence.

Meaningful agreement around a concrete proposal could make cooperation easier, especially when the strongest unresolved objections are visible too. This could concern a safety requirement, support for workers affected by AI, or how economic benefits are shared. But revealing agreement is not the same as changing incentives or securing enforceable commitments.

A longer-term direction: accountability over time

We would like to make it easier to trace how institutional commitments change, whether promised evaluations happen, and how experts update their positions. Dated sources are a foundation; systematic commitment tracking and compliance assessment require further work.

Explore the conditions for cooperation

Another possibility is conditional commitments: “I would support slowing frontier AI development if other major actors agreed and compliance could be independently verified.” Exploring those conditions could reveal possible agreements that unconditional votes miss. YouCongress does not currently offer infrastructure to make or enforce these commitments.

What exists, and what we want to investigate

How would we know this is working?

We want to test the links in this theory, not assume them. Useful evaluations would ask:

• Can researchers reconstruct a debate faster, compared with their usual methods, without losing accuracy or important minority arguments?
• Can participants explain opposing arguments and identify the actual points of disagreement more accurately?
• Do creators learn something useful about their audiences’ views and use it to address substantive questions or objections?
• Do previously overlooked contributors earn trust through useful arguments and engagement, and does this open opportunities for public service?
• Do corrections and new evidence improve the record, including its coverage of missing perspectives?
• Do decision-makers use the resource in identifiable briefings, consultations, evaluations, or decisions, and does it help them consider evidence and affected people they would otherwise miss?
• Where AI policies change, is there evidence of a contribution from YouCongress and of effective implementation that improves safety, outcomes for workers, or how benefits are shared?

More votes, greater agreement, or more time on site would not by themselves demonstrate better judgment. Apparent consensus can reflect missing voices, shared errors, or social pressure. Evidence of downstream impact will be harder to establish than product usage.

We intend to publish what we learn, including evidence that parts of this approach do not work, and revise the theory accordingly.