NLM IRP Seminar Schedule
UPCOMING SEMINARS
-
Oct. 8, 2024 Jing Wang
Enhancing Heart Failure Prediction through LLM-backed Doctor Simulation -
Oct. 15, 2024 Tanvi Patel
Generative and Diagnostic Medical Imaging through AI -
Oct. 22, 2024 Lakshminarayan Iyer
TBD -
Oct. 29, 2024 Rezarta Islamaj
TBD -
Nov. 5, 2024 Max Burroughs
TBD
RECENT SEMINARS
-
Oct. 1, 2024 Timothy Doerr
Electrostatics for biomolecular systems: ionic screening and more -
Sept. 24, 2024 Natalya Yutin
Virus discovery in the era of massive metagenomic sequencing -
Sept. 10, 2024 Diego Salazar Barreto
A phenome-wide association study to identify adverse events related with glucagon-like protein-1 agonists in Type 2 Diabetes cohort. -
July 23, 2024 Yu group
Yu Group Research Update -
July 18, 2024 Xiaofang Jiang
Jiang Lab research updates
Scheduled Seminars on Oct. 31, 2023
Contact NLM_IRP_Seminar_Scheduling@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.