Multilingual Bias in Large Language Models: Assessing Political Skew Across Languages
Srishti Dutta, Akash Kundu · Team Evil_Propaganda
Submitted to AI and Democracy Hackathon: Demonstrating the Risks. Sprint projects are early-stage work by participants, not Apart Research publications.
This project has no abstract. The report has the details.
Reviews
Good choice of research question - I think multilingual bias is an important thing for LLM developers to evaluate. Would be interesting to investigate ways of mitigating this bias, e.g. by generating synthetic politically neutral data.
nice to see some very concrete outputs!
Impressive and engaging writeup! Investigating potential uses of AI in the legislation creation process seems very timely, I imagine there’s already hundreds of low-level staffers injecting delve-ridden texts into bills all over the US. Both the process and the democratic system itself will have to adjust to mitigate the risks. A direction in which this work could be extended is trying to see how real human beings interact with {malicious-,benign-}{AI,human} produced legislation.Sadly, I could access neither the deployed app nor the sources.
Cite this project
@misc{dutta2024multilingual,
title = {{Multilingual Bias in Large Language Models: Assessing Political Skew Across Languages}},
author = {Srishti Dutta and Akash Kundu},
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/multilingual-bias-in-large-language-models-assessing-political-skew-across-languages}},
url = {https://apartresearch.com/sprints/projects/multilingual-bias-in-large-language-models-assessing-political-skew-across-languages}
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