- About Us
- Guidelines
-
The Author ensures that the research has been conducted responsibly and ethically with adherence to all relevant regulations. read more..
- For Authors
- For Reviewer
- Manuscript Guidelines
- Membership
-
- Journals
- Reprints
- e-Books
- Videos
- Indexing
- Contact Us
COVID-19
- Submissions
Abstract
COJ Robotics & Artificial Intelligence
The Value of Explanations for Machine Learning Algorithms
-
Open or CloseStockem Novo A*
Institute of Computer Science, Ruhr West University of Applied Sciences, Germany
*Corresponding author:Stockem Novo A, Institute of Computer Science, Ruhr West University of Applied Sciences, 45479 Mülheim an der Ruhr, Germany
Submission: May 24, 2022;Published: July 06, 202
Abstract
Machine Learning techniques are powerful in many different domains. For application in sensitive areas where humans are involved, the requirements regarding model understanding are strict. Currently, methods are developed that help understanding and allow drawing conclusions from the observations. Decision plots and counterfactual explanations give information about the model output, the impact of single features can be estimated. This is already a beneficial first step towards model transparency. However, there are no methods yet that use this gained knowledge as a feedback to update the model accordingly in a straight-forward manner.
Keywords: Deep learning; Bias; Fairness; Explainable AI
Editor In Chief
.jpg)
Signup for Newsletter
Quick Links
Editorial Board Registrations
Submit your Article
Best Paper of the Volume
Reprints
Refer a Friend
Advertise With UsOur Recent Edition
.jpg)
Top Editors
.jpg)
.bmp)
.jpg)
.png)
.jpg)
.png)
.png)
.png)
Financial Support
Sponsors
Latest e-Books
Latest Video
a Creative Commons Attribution 4.0 International License. Based on a work at www.crimsonpublishers.com.
Best viewed in 
| Above IE 9.0 version