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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods.
Magnus Gray1, Mariofanna Milanova2, Leihong Wu1
1Division of Bioinformatics & Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States.
JMIR Medical Informatics
|November 15, 2024
Summary
A new method, the standard deviation-word embedding association test (SD-WEAT), offers a more robust and reliable way to measure bias in artificial intelligence (AI) language models. This improved technique addresses limitations of previous methods, ensuring fairer AI development.
Area of Science:
- Natural Language Processing (NLP)
- Artificial Intelligence (AI) Ethics
- Machine Learning Model Evaluation
Background:
- Artificial intelligence (AI) systems are increasingly used across industries, but they can perpetuate and amplify societal biases.
- Existing methods for measuring bias in AI, such as the word embedding association test (WEAT), have limitations including non-robustness and reliance on predefined word groups.
- There is a critical need for improved methods to accurately measure and mitigate bias in AI to ensure fair and ethical development.
Purpose of the Study:
- To introduce a modified and more robust measure for detecting bias in AI language models.
- To address the limitations of the traditional word embedding association test (WEAT).
- To enhance the applicability and reliability of bias measurement in natural language processing embeddings.
Main Methods:
- Introduction of the standard deviation-word embedding association test (SD-WEAT), a novel modification of the WEAT.
- The SD-WEAT calculates bias by analyzing the standard deviation of multiple WEAT permutations.
- Evaluation of bias and stability in prominent language embedding models: GloVe, Word2Vec, and BERT.
Main Results:
- The SD-WEAT demonstrated comparable results to the WEAT, with high correlations in bias scores (r=0.786) and P values (r=0.776).
- The SD-WEAT overcomes WEAT limitations by removing the need for predefined attribute groups and reducing outlier impact through multiple runs.
- The new method proved to be more consistent and reliable than the original WEAT for bias assessment.
Conclusions:
- The SD-WEAT presents a promising advancement for the robust measurement of bias in AI language model embeddings.
- This method offers greater accessibility and reliability for evaluating and mitigating bias in AI.
- The SD-WEAT contributes to the development of fairer and more equitable AI technologies.
Keywords:
AIAI language modelsAI-powered toolNLPapplicationartificial intelligenceassessmentbiasbias measurementdecision-makinginput embeddingslanguage modelsnatural language processingMore Related Videos
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