From a4a75016c0362dc9a9990fe5d50c0dd537d06394 Mon Sep 17 00:00:00 2001 From: "Anh M. Nguyen" Date: Fri, 22 Apr 2022 08:44:31 -0500 Subject: [PATCH] NIST 4 principles of XAI --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 66c8bd5..e2c42f1 100644 --- a/README.md +++ b/README.md @@ -34,6 +34,7 @@ This is an on-going attempt to consolidate interesting efforts in the area of un #### Opinions * Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead _Rudin et al. Nature 2019_ [pdf](https://www.nature.com/articles/s42256-019-0048-x) * Towards falsifiable interpretability research. _Leavitt & Morcos 2020_ [pdf](https://arxiv.org/abs/2010.12016 "Issues with the current evaluation of attribution maps, feature visualization methods and Best practices for robust, falsifiable interpretability research") +* Four principles of Explainable Artificial Intelligence. _Phillips et al. 2021 (NIST.gov)_ [pdf](https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8312.pdf "An AI must provide explanations for its outputs and explanations must be meaningful/understandable to users and accurate. And the AI must know what it does not know.") #### Open research questions