From 6cfb46d04be8664f58e81bc12b22c183da7a4d9f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=91=A8=E5=89=91?= Date: Sat, 24 Sep 2022 09:58:12 +0000 Subject: [PATCH] update sigs/xai/README.md. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 周剑 --- sigs/xai/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sigs/xai/README.md b/sigs/xai/README.md index f9ee5a1..99611ad 100644 --- a/sigs/xai/README.md +++ b/sigs/xai/README.md @@ -8,7 +8,7 @@ Explainable AI (also termed transparent AI) is a form of artificial intelligence Theoretical flaws in machine learning decision-making mechanisms -Due to data samples' general limitations and biases, this association learning will inevitably learn a spurious relationship. A model based on this as a decision-making basis may perform well on most test data, but in fact, the reasoning and decision-making ability based on correct causality has not been learned, and its performance will be greatly reduced when faced with new data with distribution shift from the training samples. +Due to data samples' general limitations and biases, this association learning will inevitably learn a spurious relationship. A model based on this as a decision-making basis may perform well on most test data, but in fact, the reasoning and decision-making ability based on correct causality has not been learned, and its performance will be greatly reduced when faced with new data with distribution shift from the training samples. Application pitfalls of machine learning