Navigating AI ethics: ANN and ANFIS for transparent and accountable project evaluation amidst contesting AI practices and technologies
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1535845/full |
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| author | Sandeep Wankhade Manoj Sahni Ernesto León-Castro Maricruz Olazabal-Lugo |
| author_facet | Sandeep Wankhade Manoj Sahni Ernesto León-Castro Maricruz Olazabal-Lugo |
| author_sort | Sandeep Wankhade |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d971d45540ca4ff191d0f1d08610ce26 |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-d971d45540ca4ff191d0f1d08610ce262025-08-20T03:10:53ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-04-01810.3389/frai.2025.15358451535845Navigating AI ethics: ANN and ANFIS for transparent and accountable project evaluation amidst contesting AI practices and technologiesSandeep Wankhade0Manoj Sahni1Ernesto León-Castro2Maricruz Olazabal-Lugo3Department of Mathematics, Pandit Deendayal Energy University, Gandhinagar, IndiaDepartment of Mathematics, Pandit Deendayal Energy University, Gandhinagar, IndiaFaculty of Economics and Administrative Sciences, Universidad Católica de la Santísima Concepción, Concepción, ChileUnidad Regional Culiacán, Universidad Autónoma de Occidente, Culiacán, MexicoIntroductionThe 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.https://www.frontiersin.org/articles/10.3389/frai.2025.1535845/fullartificial intelligence (AI)artificial neural networks (ANN)adaptive neuro-fuzzy inference systems (ANFIS)accountabilityorganizational structures |
| spellingShingle | Sandeep Wankhade Manoj Sahni Ernesto León-Castro Maricruz Olazabal-Lugo Navigating AI ethics: ANN and ANFIS for transparent and accountable project evaluation amidst contesting AI practices and technologies Frontiers in Artificial Intelligence artificial intelligence (AI) artificial neural networks (ANN) adaptive neuro-fuzzy inference systems (ANFIS) accountability organizational structures |
| title | Navigating AI ethics: ANN and ANFIS for transparent and accountable project evaluation amidst contesting AI practices and technologies |
| title_full | Navigating AI ethics: ANN and ANFIS for transparent and accountable project evaluation amidst contesting AI practices and technologies |
| title_fullStr | Navigating AI ethics: ANN and ANFIS for transparent and accountable project evaluation amidst contesting AI practices and technologies |
| title_full_unstemmed | Navigating AI ethics: ANN and ANFIS for transparent and accountable project evaluation amidst contesting AI practices and technologies |
| title_short | Navigating AI ethics: ANN and ANFIS for transparent and accountable project evaluation amidst contesting AI practices and technologies |
| title_sort | navigating ai ethics ann and anfis for transparent and accountable project evaluation amidst contesting ai practices and technologies |
| topic | artificial intelligence (AI) artificial neural networks (ANN) adaptive neuro-fuzzy inference systems (ANFIS) accountability organizational structures |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1535845/full |
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