ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Logic and Reasoning in AI
Volume 8 - 2025 | doi: 10.3389/frai.2025.1535845
Navigating AI Ethics: ANN and ANFIS for Transparent and Accountable Project Evaluation amidst Contesting AI Practices and Technologies
Provisionally accepted
- 1 Parul University, Waghodia, Gujarat, India
- 2 Pandit Deendayal Energy University, Gandhinagar, Gujarat, India
- 3 Universidad Catolica de la Santisima Concepcion, concepcion, Chile
- 4 Universidad Autonoma de Occidente, Culiacan, Mexico
You have multiple emails registered with Frontiers:
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
In the fast evolving field of Artificial Intelligence (AI), ethical practices are critical for responsible project deployment. This paper explores the nexus of ethics and AI initiatives, addressing the complexity of quantifying ethical criteria amid uncertainty. It examines how communicative practices, organizational structures, and enabling technologies contest AI, framing its societal implications. Using Artificial Neural Networks (ANN) to assess project performance and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to model uncertainties via Fuzzy logic, the study develops a framework for ethical AI evaluation. A Fuzzy weighted average approach navigates challenges in measuring transparency, fairness, accountability, privacy, security, explainability, human involvement, and societal impact. The research investigates AI's role in reshaping organizational communication and decision-making, contextualizing ethics within enabling technologies and structures. By integrating ANN and ANFIS, this work enhances understanding of AI's ethical dimensions and proposes a path for accountable, innovative AI systems.
Keywords: accountability, AI-powered projects, Artificial intelligence (AI), Artificial neural networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), AI Innovation, Communicative practices, Organizational structures
Received: 05 Feb 2025; Accepted: 02 Apr 2025.
Copyright: © 2025 Wankhade, Sahni, Leon-Castro and Olazabal-Lugo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Manoj Sahni, Pandit Deendayal Energy University, Gandhinagar, 382 007, Gujarat, India
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
