Version 1
: Received: 23 April 2020 / Approved: 25 April 2020 / Online: 25 April 2020 (02:57:06 CEST)
How to cite:
Dutta, P.; Muppalaneni, N.B.; Patgiri, R. A Survey on Explainability: Why Should We Believe the Accuracy of A Model?. Preprints2020, 2020040456. https://doi.org/10.20944/preprints202004.0456.v1
Dutta, P.; Muppalaneni, N.B.; Patgiri, R. A Survey on Explainability: Why Should We Believe the Accuracy of A Model?. Preprints 2020, 2020040456. https://doi.org/10.20944/preprints202004.0456.v1
Dutta, P.; Muppalaneni, N.B.; Patgiri, R. A Survey on Explainability: Why Should We Believe the Accuracy of A Model?. Preprints2020, 2020040456. https://doi.org/10.20944/preprints202004.0456.v1
APA Style
Dutta, P., Muppalaneni, N.B., & Patgiri, R. (2020). A Survey on Explainability: Why Should We Believe the Accuracy of A Model?. Preprints. https://doi.org/10.20944/preprints202004.0456.v1
Chicago/Turabian Style
Dutta, P., Naresh Babu Muppalaneni and Ripon Patgiri. 2020 "A Survey on Explainability: Why Should We Believe the Accuracy of A Model?" Preprints. https://doi.org/10.20944/preprints202004.0456.v1
Abstract
The world has been evolving with new technologies and advances day-by-day. With the advent of various learning technologies in every field, the research community is able to provide solution in every aspect of life with the applications of Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, etc. However, with such high achievements, it is found to lag behind the ability to provide explanation against its prediction. The current situation is such that these modern technologies are able to predict and decide upon various cases more accurately and speedily than a human, but failed to provide an answer when the question of why to trust its prediction is put forward. In order to attain a deeper understanding into this rising trend, we explore a very recent and talked-about novel contribution which provides rich insight on a prediction being made -- ``Explainability.'' The main premise of this survey is to provide an overview for researches explored in the domain and obtain an idea of the current scenario along with the advancements published to-date in this field. This survey is intended to provide a comprehensive background of the broad spectrum of Explainability.
Keywords
Artificial Intelligence; Explainability; Deep Learning; Machine Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
