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Chahat Raj
George Mason University
A
Anjishnu Mukherjee
George Mason University
S
Sina Mansouri
George Mason University
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Bias experts on LinkedIn & GitHub
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Founder's Pitch
"KnowBias reduces social biases in LLMs through neuron enhancement, preserving model performance."
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Why It Matters
This research addresses the critical issue of bias in large language models, which is essential for the responsible deployment of AI systems in sensitive applications, thereby meeting both ethical standards and improving user trust.
Product Angle
Develop an API or plugin for AI developers to easily integrate bias mitigation into their LLM-backed applications, ensuring ethical AI deployment.
Disruption
KnowBias could replace or augment existing debiasing technologies that focus on neuron-level suppression, offering a more robust and efficient solution.
Product Opportunity
There is significant market demand from enterprises needing compliance with fairness standards in AI. Customers include tech companies integrating LLMs, AI ethics boards, and companies providing AI-driven customer services.
Use Case Idea
Integrate KnowBias into existing LLM deployments (e.g., chatbots, content moderation tools) to reduce bias and improve fairness in automated interactions.
Science
KnowBias leverages a new approach by enhancing neurons that recognize bias rather than suppressing those that manifest bias. This is achieved using a small set of bias-knowledge questions, which identify neurons involved in bias recognition. These neurons are then enhanced at inference time to guide the model towards less biased outputs.
Method & Eval
The method involves attribution-based analysis of neurons using simple bias-knowledge questions, enhancing specific neurons during inference without training the model, empirically validated against several social bias benchmarks and LLMs, demonstrating state-of-the-art results.
Caveats
The method relies on the assumption that bias knowledge is consistently encoded in neurons across different models, which may not be universally true. It also requires careful design of bias-knowledge questions.
Author Intelligence
Jinhao Pan
LEAD George Mason University
jpan23@gmu.edu
Chahat Raj
George Mason University
Anjishnu Mukherjee
George Mason University
Sina Mansouri
George Mason University
Bowen Wei
George Mason University
Shloka Yada
Lightridge High School
Ziwei Zhu
George Mason University