FAIRSCAPE: An Evolving AI-readiness Framework for Biomedical Research
2024
FAIRSCAPE: An Evolving AI-readiness Framework for Biomedical Research
publication
Author Information
Author(s): Al Manir Sadnan, Levinson Maxwell Adam, Niestroy Justin, Churas Christopher, Parker Jillian A., Clark Timothy
Primary Institution: Cold Spring Harbor Laboratory
Hypothesis
AI applications in biomedical research require explainability for ethical deployment.
Conclusion
The FAIRSCAPE framework effectively generates and integrates essential metadata for AI model training in biomedical research.
Supporting Evidence
- The FAIRSCAPE framework provides ethical and semantic characterization of datasets.
- It integrates with NIH-recommended repositories and is cloud-compliant.
Takeaway
This study created a tool that helps researchers use AI responsibly by providing important information about the data they use.
Methodology
The framework generates and packages XAI metadata, including provenance graphs and data dictionaries.
Digital Object Identifier (DOI)
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