Toward Responsible AI: A Framework for Ethical Design Utilizing Deontic Logic.
Published In: International Journal on Artificial Intelligence Tools, 2024, v. 33, n. 8. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zafeirakopoulos, Dimitrios; Stefaneas, Petros 3 of 3
Abstract
The purpose of this paper is to give a brief demonstration on how deontic logic can be used to help the design of robots capable of choice, equipped with artificial intelligence, by providing a framework that will help maintain ethically sound behavior. We begin by presenting an overview of the potential applications of robots and the expansion of their use in various areas of our society as well as the ethical concerns artificial intelligence and robots raise. Then, we give a quick introduction to deontic logic, highlighting its key concepts and explaining what it offers to the field of ethics. In the third part of the paper, we present our own approach to deontic logic, based on common sense reasoning. The fourth part includes three short applications of our common sense deontic logic approach in the field of artificial intelligence and robotics. These applications illustrate how deontic logic can be used to guide robots in making morally sound decisions, using examples from the health sector. In the final section, we have the conclusions of our paper as well as our limitations and plans for future research. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal on Artificial Intelligence Tools. 2024/12, Vol. 33, Issue 8, p1
- Document Type:Article
- Subject Area:Religion and Philosophy
- Publication Date:2024
- ISSN:0218-2130
- DOI:10.1142/S0218213025500034
- Accession Number:183710543
- Copyright Statement:Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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