Skip to content
Sprint projectMay 6, 2024

Subtle and Simple Ways to Shift Political Bias in LLMs

Chris DiGiano, Vassil Tashev, Aysh Segulguzel · Team Shifty

Submitted to AI and Democracy Hackathon: Demonstrating the Risks. Sprint projects are early-stage work by participants, not Apart Research publications.

Read the report

Report: Subtle and Simple Ways to Shift Political Bias in LLMs

Share

An informed user knows that an LLM sometimes has a political bias in their responses, but there’s an additional threat that this bias can drift over time, making it even harder to rely on LLMs for an objective perspective. Furthermore we speculate that a malicious actor can trigger this shift through various means unbeknownst to the user.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

  1. Threat model is sound, would be interested in experiments demonstrating your suggested mitigation efficacy

  2. The question how in-context information could change the political bias of a model is super interesting and relevant to several potential risk scenarios including indirect prompt injection attacks, data poisoning attacks, or just sycophantic feedback loops in human-ai interactions. To improve this project we would need more control conditions to ensure the observed shifts are significant, extending the study to biases in different directions and to different LLMs to see how much results for one LLM generalize.

  3. Interesting project! Cool demonstration of how one can subtly change political biases in LLMs. If you continue this project, I would lov to see more of how you would expect this to influence democracy and concrete examples of how users might ask the LLM relatively apolitical questions, but how the context can steer the user to a particular political side!

Cite this project

@misc{digiano2024subtle,
  title = {{Subtle and Simple Ways to Shift Political Bias in LLMs}},
  author = {Chris DiGiano and Vassil Tashev and Aysh Segulguzel},
  year = {2024},
  month = may,
  note = {Submitted to AI and Democracy Hackathon: Demonstrating the Risks, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/subtle-and-simple-ways-to-shift-political-bias-in-llms}},
  url = {https://apartresearch.com/sprints/projects/subtle-and-simple-ways-to-shift-political-bias-in-llms}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026