Publicação
Applications, Challenges, and Ethical Implications of Generative AI: A Systematic Review
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Naranjo-Zolotov, Mijail Juanovich | |
| dc.contributor.author | Esteves, Rodrigo Manuel Abreu | |
| dc.date.accessioned | 2024-10-29T12:23:39Z | |
| dc.date.available | 2024-10-29T12:23:39Z | |
| dc.date.issued | 2024-10-22 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management | pt_PT |
| dc.description.abstract | Generative AI has emerged as a transformative technology with far-reaching implications across various domains. This systematic literature review provides a comprehensive analysis of the current state of generative AI, examining its developments, applications, challenges, and ethical considerations. The review analyses 69 recent publications from 2020-2024, focusing on key models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Large Language Models (LLMs). The findings reveal widespread applications of generative AI across healthcare, social sciences, computer science, and business sectors. In healthcare, generative AI enhances medical diagnostics, personalized treatment plans, and drug discovery. In education, it supports personalized learning and automated assessment. In business, it improves customer service, financial forecasting, and fraud detection. However, significant challenges persist, including bias and fairness issues, data privacy and security concerns, and the need for transparency and interpretability in AI systems. Ethical considerations surrounding bias, privacy, transparency, and potential misuse are extensively discussed. The review also explores future directions, emphasizing the development of ethical guidelines, improved model capabilities, domainspecific integration, and interdisciplinary collaboration. This review contributes to the ongoing dialogue on responsible AI deployment and the societal implications of cutting-edge technologies. It provides a comprehensive resource for researchers, practitioners, and policymakers navigating the evolving landscape of generative AI, highlighting the need to balance innovation with ethical considerations and regulatory frameworks. | pt_PT |
| dc.identifier.tid | 203777514 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/174252 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Generative AI | pt_PT |
| dc.subject | Large Language Models | pt_PT |
| dc.subject | Ethical AI | pt_PT |
| dc.subject | Artificial Intelligence Applications | pt_PT |
| dc.subject | AI Challenges | pt_PT |
| dc.subject | Systematic Review | pt_PT |
| dc.subject | SDG 3 - Good health and well-being | pt_PT |
| dc.subject | SDG 4 - Quality education | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 16 - Peace, justice and strong institutions | pt_PT |
| dc.subject | SDG 17 - Partnerships for the goals | pt_PT |
| dc.title | Applications, Challenges, and Ethical Implications of Generative AI: A Systematic Review | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Gestão dos Sistemas e Tecnologias de Informação | pt_PT |