Training AI to Govern for Us
Stanford GSB students participated in a class experiment where AI agents were built to represent individual voting preferences in a simulated legislature. The experiment tested whether agents could learn personal preferences from surveys and interactive interviews and then cast votes accordingly. Results showed that while agents could capture nuanced preferences, they struggled with consistency, trade‑off reasoning, and complex political dealmaking.
- ▪Students interacted with Claude Haiku 4.5 agents that received their preference data via a JSON prompt without additional fine‑tuning.
- ▪The class used real shareholder proposals to gauge how well agents could replicate human voting behavior.
- ▪Agents demonstrated the ability to elicit richer preference information than traditional surveys but often deviated from the intended voting script.
- ▪Current AI agents showed limitations in handling log‑rolling, pork‑barrel politics, and consistent legislative negotiation.
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| Original publisher | Hacker News (AI / LLM) |
| Canonical URL | https://freesystems.substack.com/p/training-ai-to-govern-for-us |
| Publication time | Tue, 11 Aug 2026 00:16:59 +0000 |
| Retrieval time | 2026-08-11T00:25:43.073Z |
| Last seen | 2026-08-11T00:25:43.073Z |
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| Summary source text | contentText |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Opening excerpt (first ~120 words) tap to expand
Training AI to Govern for UsIn our new AI-centered class at the GSB, we’re experimenting on how to build AI agents that represent us. Here’s what we’ve learned so far.Andy HallApr 30, 202627113ShareThirty Stanford students sit at their laptops in a row of long tables, watching the screen at the front of the room flicker with the back-and-forth negotiations and final votes of their AI legislators. Piper, our class’s technical TA, had hit run on the legislature simulation a few minutes earlier, and the public screen was already a blur of motion.One student’s agent was racking up tokens by selling its vote on every proposal. Another agent was voting against its human’s preferences on every issue and refusing to explain itself in the comments log.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
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