Wankhade Sandeep, Sahni Manoj, León-Castro Ernesto, Olazabal-Lugo Maricruz
Department of Mathematics, Pandit Deendayal Energy University, Gandhinagar, India.
Faculty of Economics and Administrative Sciences, Universidad Católica de la Santísima Concepción, Concepción, Chile.
Front Artif Intell. 2025 Apr 30;8:1535845. doi: 10.3389/frai.2025.1535845. eCollection 2025.
The rapid evolution of Artificial Intelligence (AI) necessitates robust ethical frameworks to ensure responsible project deployment. This study addresses the challenge of quantifying ethical criteria in AI projects amidst contesting communicative practices, organizational structures, and enabling technologies, which shape AI's societal implications.
We propose a novel framework integrating Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to evaluate AI project performance and model ethical uncertainties using Fuzzy logic. A Fuzzy weighted average approach quantifies critical ethical dimensions: transparency, fairness, accountability, privacy, security, explainability, human involvement, and societal impact.
The framework enables a structured assessment of AI projects, enhancing transparency and accountability by mapping ethical criteria to project outcomes. ANN evaluates performance metrics, while ANFIS models uncertainties, providing a comprehensive ethical evaluation under complex conditions.
By combining ANN and ANFIS, this study advances the understanding of AI's ethical dimensions, offering a scalable approach for accountable AI systems. It reframes organizational communication and decision-making, embedding ethics within AI's technological and structural contexts. This work contributes to responsible AI innovation, fostering trust and societal alignment in AI deployments.
人工智能（AI）的迅速发展需要强大的伦理框架，以确保项目的负责任部署。本研究应对了在相互竞争的交流实践、组织结构和支撑技术中量化人工智能项目伦理标准的挑战，这些因素塑造了人工智能的社会影响。
我们提出了一个整合人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）的新颖框架，以使用模糊逻辑评估人工智能项目性能并对伦理不确定性进行建模。模糊加权平均方法量化了关键的伦理维度：透明度、公平性、问责制、隐私、安全性、可解释性、人类参与度和社会影响。
该框架能够对人工智能项目进行结构化评估，通过将伦理标准映射到项目成果来提高透明度和问责制。人工神经网络评估性能指标，而自适应神经模糊推理系统对不确定性进行建模，在复杂条件下提供全面的伦理评估。
通过结合人工神经网络和自适应神经模糊推理系统，本研究推进了对人工智能伦理维度的理解，为可问责的人工智能系统提供了一种可扩展的方法。它重新构建了组织沟通和决策，将伦理嵌入人工智能的技术和结构背景中。这项工作有助于负责任的人工智能创新，在人工智能部署中促进信任和社会协调。
