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Notes on ai bias
6 notes
Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages
The paper discusses how AI tools for education and language support can disadvantage speakers of underrepresented languages before training, using Bengali as a case study. The authors identify four main issues: a web…
Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts
The text introduces a new method called Poli-Bias, which is designed to measure bias in large language models (LLMs) when dealing with international political conflicts. This framework compares responses to paired…
Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias
A research study presents an approach to understanding and mitigating bias in large language models (LLMs) used as judges, focusing on the hidden state of these models rather than just their input-output level. The…
Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias
The research investigates bias in Large Language Models (LLMs) acting as judges by exploring representation-level accounts, focusing on the hidden state of the LLM, complementing input-output level studies. The study…
Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation
The paper introduces Distill to Detect (D2D), a method that helps identify hidden biases in language models used in high-stakes roles by amplifying these biases through cartridge distillation. The method is designed to…
Self-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA
The authors investigate the generation of question-answer (QA) pairs used for training language models and find that this process is not neutral as it can introduce biases due to the selection of evidence and the…
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