A Framework for Explanation of Machine Learning Decisions

By C. Brinton

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Raghu N. Kacker
2. D. Richard Kuhn
3. Dimitris E. Simos
4. Yu Lei
5. R. Dersimonian

Citations in the corpus

• Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “(Assad, Carmichael, Kay, and Kummerfield, 2007; Brinton, 2017; Clos, Wiratunga, and Massie, 2017; Codella, et al., 2018; Craven and Shavlik, 1996; Fukui et al., 2016; Hu, Andreas, Rohrbach, Darrell, and Saenko, 2017; Jha, Raman, Pinto, Sahai, and Francis, 2017; Kim, 2018; Kim, Shah, and…”
• Combinatorial Methods for Explainable AI (2020, background): “Consequently, there is a significant need for improvements in explainability of AI/ML system functions and decisions [1][2][3][4][5][6][7].”