Considerations in the reliability and fairness audits of predictive models for advance care planning

• मुख्य लेखकों: Jonathan Lu (लेखक), Amelia Sattler (लेखक), Samantha Wang (लेखक), Ali Raza Khaki (लेखक), Alison Callahan (लेखक), Scott Fleming (लेखक), Rebecca Fong (लेखक), Benjamin Ehlert (लेखक), Ron C. Li (लेखक), Lisa Shieh (लेखक), Kavitha Ramchandran (लेखक), Michael F. Gensheimer (लेखक), Sarah Chobot (लेखक), Stephen Pfohl (लेखक), Siyun Li (लेखक), Kenny Shum (लेखक), Nitin Parikh (लेखक), Priya Desai (लेखक), Briththa Seevaratnam (लेखक), Melanie Hanson (लेखक), Margaret Smith (लेखक), Yizhe Xu (लेखक), Arjun Gokhale (लेखक), Steven Lin (लेखक), Michael A. Pfeffer (लेखक), Winifred Teuteberg (लेखक), Nigam H. Shah (लेखक)
• स्वरूप: पुस्तक
• प्रकाशित: Frontiers Media S.A., 2022-09-01T00:00:00Z.
• विषय: article
• ऑनलाइन पहुंच: Connect to this object online.

Multiple reporting guidelines for artificial intelligence (AI) models in healthcare recommend that models be audited for reliability and fairness. However, there is a gap of operational guidance for performing reliability and fairness audits in practice. Following guideline recommendations, we condu...