Navigating AI ethics: ANN and ANFIS for transparent and accountable project evaluation amidst contesting AI practices and technologies - Wankhade, Sandeep; Sahni, Manoj; León-Castro, Ernesto; Olazabal-Lugo, Maricruz (Frontiers in Artificial Intelligence, 2025)
IntroductionThe 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.MethodsWe 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.ResultsThe 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.DiscussionBy 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.