DeepAI AI Chat
Log In Sign Up

Do Human Rationales Improve Machine Explanations?

05/31/2019
by   Julia Strout, et al.
The University of Texas at Austin
0

Work on "learning with rationales" shows that humans providing explanations to a machine learning system can improve the system's predictive accuracy. However, this work has not been connected to work in "explainable AI" which concerns machines explaining their reasoning to humans. In this work, we show that learning with rationales can also improve the quality of the machine's explanations as evaluated by human judges. Specifically, we present experiments showing that, for CNN- based text classification, explanations generated using "supervised attention" are judged superior to explanations generated using normal unsupervised attention.

READ FULL TEXT

page 1

page 2

page 3

page 4

03/16/2020

Harnessing Explanations to Bridge AI and Humans

Machine learning models are increasingly integrated into societally crit...
06/21/2021

A Turing Test for Transparency

A central goal of explainable artificial intelligence (XAI) is to improv...
12/10/2020

DAX: Deep Argumentative eXplanation for Neural Networks

Despite the rapid growth in attention on eXplainable AI (XAI) of late, e...
05/23/2020

Towards Analogy-Based Explanations in Machine Learning

Principles of analogical reasoning have recently been applied in the con...
11/27/2020

Reflective-Net: Learning from Explanations

Humans possess a remarkable capability to make fast, intuitive decisions...
04/16/2021

Towards Human-Understandable Visual Explanations:Imperceptible High-frequency Cues Can Better Be Removed

Explainable AI (XAI) methods focus on explaining what a neural network h...
02/24/2021

Teach Me to Explain: A Review of Datasets for Explainable NLP

Explainable NLP (ExNLP) has increasingly focused on collecting human-ann...