From theory to practice: Harmonizing taxonomies of trustworthy AI
The increasing capabilities of AI pose new risks and vulnerabilities for organizations and decision makers. Several trustworthy AI frameworks have been created by U.S. federal agencies and international organizations to outline the principles to which AI systems must adhere for their use to be consi...
Saved in:
Main Authors: | Christos A. Makridis (Author), Joshua Mueller (Author), Theo Tiffany (Author), Andrew A. Borkowski (Author), John Zachary (Author), Gil Alterovitz (Author) |
---|---|
Format: | Book |
Published: |
Elsevier,
2024-12-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Editorial: Trustworthy AI for healthcare
by: Oleg Agafonov, et al.
Published: (2024) -
The unmet promise of trustworthy AI in healthcare: why we fail at clinical translation
by: Valerie K. Bürger, et al.
Published: (2024) -
A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare
by: Jana Fehr, et al.
Published: (2024) -
Assessing Trustworthiness of Personal Aides
by: Denise Clark Lewis PhD, et al.
Published: (2011) -
A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study
by: Marlene Blaß, et al.
Published: (2024)