NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Jan. 14, 2025 Ryan Bell
Comprehensive analysis of the YprA-like helicase family provides deep insight into the evolution and potential mechanisms of widespread and largely uncharacterized prokaryotic antiviral defense systems -
Jan. 16, 2025 Qingqing Zhu
GPTRadScore and CT-Bench: Advancing Multimodal AI Evaluation and Benchmarking in CT Imaging -
Jan. 17, 2025 Xuegong Zhang
Using Large Cellular Models to Understand Cell Transcriptomics Language -
Jan. 21, 2025 Qiao Jin
Artificial Intelligence for Evidence-based Medicine -
Jan. 28, 2025 Kaleb Abram
TBD
RECENT SEMINARS
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Jan. 14, 2025 Ryan Bell
Comprehensive analysis of the YprA-like helicase family provides deep insight into the evolution and potential mechanisms of widespread and largely uncharacterized prokaryotic antiviral defense systems -
Dec. 17, 2024 Joey Thole
Training set associations drive AlphaFold initial predictions of fold-switching proteins -
Dec. 10, 2024 Amr Elsawy
AI for Age-Related Macular Degeneration on Optical Coherence Tomography -
Dec. 3, 2024 Sarvesh Soni
Toward Relieving Clinician Burden by Automatically Generating Progress Notes -
Nov. 19, 2024 Benjamin Lee
Reiterative Translation in Stop-Free Circular RNAs
Scheduled Seminars on Oct. 31, 2023
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Along with the dramatic growth of interest in artificial intelligence and deep learning is a growth in questions about such algorithms. For example, are they fair? A machine learning algorithm is behind PubMed search's Best Match algorithm. NIST's AI Risk Management Framework points out that fairness needs to be regularly measured and tracked across changes in algorithms. We measured fairness in PubMed search in the areas of article language and journal ranking. We also modified the search algorithm by changing which clicks are used to score articles and adding a dense retrieval feature. We measure the effect on fairness resulting from these changes. We conclude with a discussion of the implementation and implications of a common suggestion for balancing fairness and relevance of search results.