Skip to content
Sprint projectMay 6, 2024

Silent Curriculum

Aman Priyanshu, Supriti Vijay · Team Silent Curriculum

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

Our project, "The Silent Curriculum," reveals how LLMs (Large Language Models) may inadvertently shape education and perpetuate cultural biases. Using GPT-3.5 and LLaMA2-70B, we generated children's stories and analyzed ethnic-occupational associations [self-annotated ethnicity extraction]. Results show strong similarities in biases across LLMs [cosine similarity: 0.87], suggesting an AI monoculture that could narrow young minds. This algorithmic homogenization [convergence of pre-training data, fine-tuning datasets, analogous guardrails] risks creating echo chambers, threatening diversity and democratic values. We urgently need diverse datasets and de-biasing techniques [mitigation strategies] to prevent LLMs from becoming unintended arbiters of truth, stifling the multiplicity of voices essential for a thriving democracy.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Reviews of strong projects are shared with funders, as you choose below.

Not shown on this page.

Shown on this page, without your name.

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

Decline to score

Only when the quality of the writing makes the work impossible to follow. Say why in your private critique; nothing shows on this page.

Sharing your name *

When we share a strong project with funders, your name, scores and public critique go with it, confidentially; this choice is only about whether your name is public. If you think this project deserves funding, please also endorse it: that carries the most weight.

Share my private critique with funders *

  1. Interesting experiment that shows how LLMs may have biases in generating childrens stories. I think for this hackathon, the experiments are bit far removed from a direct threat to democracy. It would also be interesting to see how you expect this to change in the feature and what can be done to mitigate the consequences of these biases.

Cite this project

@misc{priyanshu2024silent,
  title = {{Silent Curriculum}},
  author = {Aman Priyanshu and Supriti Vijay},
  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/silent-curriculum}},
  url = {https://apartresearch.com/sprints/projects/silent-curriculum}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026