Speaker(s): 

Ricky Mouser, MS, PhD- Hecht Levi Fellow at the Berman Institute of Bioethics at Johns Hopkins - has nothing to disclose

Learning Objectives: 

At the conclusion of this session, the participant should be able to: 

1. Describe the role of fairness metrics in benchmarking AI model performance.

2. Distinguish between uses of fairness metrics as definitions of fairness versus evidence of fairness.

3. Analyze how social micropractices structuring AI research contribute to a larger “benchmarking culture.”

4. ​Recommend interdisciplinary interventions to support metrological choices with sound epistemic arguments.

Disclosure of Relevant Financial Relationships with/without Commercial Interests:

The Planning Committee consisting of Michelle Meyer, PhD, JD, HEC-C, F. Daniel Davis, PhD, HEC-C, Sharon Gray, MA, BSN, RN, HEC-C, Daniel J. Hoegen, MSW, LSW, HEC-C, Christopher Chabris, PhD, Greg Burke, MD, John Pagnotto, MD, Elizabeth McDonald, MS, RDN, LDN, CNSC, Linda Page, RT, Ronald Napikoski, RT, Brian Simpkins, PharmD, BCPS, Kaylee Kachurka, PA-C, Erin Hall- Melnychuk, PhD, Anne Kasper, LPC, BBC have no identified disclosures. 

CE Committee Member/Content Reviewers have nothing to disclose: Angela Slampak-Cindric, PharmD, BCPS, Gale Shalongo, DNP, MSN, RN, ACNS, BC

Any/All relevant financial relationships have been mitigated. 

Content Disclosure: 

This presentation/content is HIPAA compliant. 

Commercial Support for this Session

None