Magnus Gray
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Researcher
Also affiliated: American National Standards Institute (2001); Defense Advanced Research Projects Agency (2001); United States Food and Drug Administration (2023–2024); National Institute of Standards and Technology (2001); United States Department of Commerce (2001); Information Technology Laboratory (2001); Environmental Research Institute (2001)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Magnus Gray's research focuses on the application of artificial intelligence and natural language processing techniques within regulatory science, particularly concerning drug labeling and the assessment of bias in AI models. He has published work on classifying free text in regulatory documents and developed frameworks for integrating large language models into regulatory environments to enhance transparency and trustworthiness.
His work also investigates methods for measuring and mitigating bias in AI systems, with specific applications to healthcare AI models and their embeddings across sensitive populations. Gray has explored the use of techniques like SD-WEAT for bias detection and benchmarking. Additionally, his research includes comparative studies of molecular descriptors and AI-based embeddings for toxicity prediction.
Metrics
- h-index: 6
- Publications: 14
- Citations: 95
Selected Publications
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Comparative Study of Molecular Descriptors and AI-Based Embeddings for Toxicity Prediction (2025)
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Benchmarking bias in embeddings of healthcare AI models: using SD-WEAT for detection and measurement across sensitive populations (2025)
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Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods (2024)
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SD-WEAT: Towards Robustly Measuring Bias in Input Embeddings (2024)
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A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document (2024)
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RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling (2023)
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Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science (2023)
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Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents (2023)
Collaboration Network
Top Collaborators
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents
- Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods
Showing 5 of 8 shared publications
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods
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