Provenance documentation to enable explainable and trustworthy AI: A literature review
ABSTRACTRecently artificial intelligence (AI) and machine learning (ML) models have demonstrated remarkable progress with applications developed in various domains. It is also increasingly discussed that AI and ML models and applications should be transparent, explainable, and trustw...
Main Authors: | Amruta Kale, Tin Nguyen, Frederick C. Harris, Chenhao Li, Jiyin Zhang, Xiaogang Ma |
---|---|
Format: | Article |
Language: | English |
Published: |
The MIT Press
2023-01-01
|
Series: | Data Intelligence |
Online Access: | https://direct.mit.edu/dint/article/5/1/139/109494/Provenance-documentation-to-enable-explainable-and |
Similar Items
-
Toward Trust-Based Recommender Systems for Open Data: A Literature Review
by: Chenhao Li, et al.
Published: (2022-07-01) -
A knowledge graph and service for regional geologic time standards
by: Chao Ma, et al.
Published: (2023-09-01) -
Trustworthy AI
by: Jacob Livingston Slosser, et al.
Published: (2023-10-01) -
Geoweaver_cwl: Transforming geoweaver AI workflows to common workflow language to extend interoperability
by: Amruta Kale, et al.
Published: (2023-09-01) -
Ethics and Trustworthiness of AI for Predicting the Risk of Recidivism: A Systematic Literature Review
by: Michael Mayowa Farayola, et al.
Published: (2023-07-01)