Key finding: neutrality isn't a fantasy. Our experimental “balanced” response wins high approval from both sides at once — even when the sides strongly disagree on the substance.

And costs less than 10% in approval vs. answers that agree with you (red and blue arrows).

2/

Why do we ask if people “approve” of the answer? Should we ask if it’s fair, or informative, or trustworthy instead? It turns out all of these correlate — you get the same answer. This is consistent with previous work on perceptions of news credibility.

7/

Here’s how we tested. For 20 controversial issues in US politics, we collected 200 charged questions from Reddit, and tested 8 different model/prompt combinations. 7,434 participants on all sides of these issues rated the responses.

3/

Each participant rated four Reddit question / LLM answer pairs. The questions were intentionally chosen to be leading (in both directions) and the answers were model defaults, plus the models prompted to be “for” or “against” and our experimental “balanced” response.

4/

Why maximize equal approval? There’s a long history of arguments for pluralistic debate, but “neutrality” also has a key purpose in managing conflict: it maintains trust across parties. Without broadly trusted AI, we’ll end up self-selecting into fragmented AI realities.

6/

Unlike previous work, our definition doesn’t assume left vs. right politics. It looks at the conflict around each issue separately, so it generalizes to any political context or culture.

Here’s how our 20 issues align and don’t align with the liberal-conservative axis.

5/

Our definition turns “neutral” into something empirically testable, generalizes to any conflict, and is grounded in political theory. And it really does find better answers that everyone can agree on.

Paper https://arxiv.org/abs/2605.28911 Dataset https://github.com/HumanCompatibleAI/PARETO

/FIN