Sustainability and Artificial Intelligent-based Systems
Abstract
Artificial intelligence (AI) has evolved with advancements such as deep learning, Python, and deep neural networks. These advancements have driven the rise of AI, with developments like XAI, Small Data, and ImageNet. Generative AI is changing the economy and is expected to have a significant impact in areas such as content creation, software development, and marketing. The European Commission proposes AI regulation based on a risk approach, with different levels of risk and requirements according to the AI category. Software sustainability is multidimensional, covering technical, economic, environmental, and social aspects. Explainability and transparency in AI models are crucial to ensure accountability and trust in their use. Integrating AI systems with legacy systems and processes involves technical and economic considerations and can generate benefits such as process optimization but also requires significant investments.
Downloads
References
Bank of America Corporation (2023). Artificial intelligence: a real game changer. https://business.bofa.com/en-us/content/economic-impact-of-ai.html
CB Insights (2023). AI 100: The most promising artificial intelligence startups of 2023. https://www.cbinsights.com/research/artificial-intelligence-top-startups-2023/
Clarke, R. (2019). Regulatory alternatives for AI. Computer Law & Security Review, 35(4), 398-409. https://doi.org/10.1016/j.clsr.2019.04.008
Condori-Fernández, N. & Lago, P. (2018). Characterizing the contribution of quality requirements to software sustainability. Journal of Systems and Software, 137, 289-305. https://doi.org/10.1016/j.jss.2017.12.005
European Parliamentary Research Service (2023). Artificial intelligence act. https://www.europarl.europa.eu/RegData/etudes/BRIE/2021/698792/EPRS_BRI(2021)698792_EN.pdf
Franch, X., Jedlitschka, A., & Martínez-Fernández, S. (2023). A requirements engineering perspective to AI-based systems development: a vision paper. En A. Ferrari & B. Penzenstadler (Eds.), Requirements engineering: foundation for software quality (pp. 223-232). Springer. https://doi.org/10.1007/978-3-031-29786-1_15
Martínez-Fernández, S., Franch, X., & Durán, F. (2023). Towards green AI-based software systems: an architecture-centric approach (GAISSA). En 49th Euromicro Conference on Software Engineering and Advanced Applications - SEAA (pp. 432-439). https://doi.org/10.1109/SEAA60479.2023.00071
OECD. (2023). Recommendation of the Council on artificial intelligence. OECD Legal Instruments: https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449#mainText
Raschka, S., Patterson, J. & Nolet, C. (2020). Machine learning in Python: main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 193. https://doi.org/10.3390/info11040193
Rolf, B., Jackson, I., Müller, M., Lang, S., Reggelin, T. & Ivanov, D. (2023). A review on reinforcement learning algorithms and applications in supply chain management. International Journal of Production Research, 61(20), 7151-7179. DOI: 10.1080/00207543.2022.2140221
Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J. (2019). Explainable AI: a brief survey on history, research areas, approaches and challenges. En J. Tang, M.Y. Kan, D. Zhao, S. Li & H. Zan (Eds.), Natural language processing and chinese computing (pp. 563-574). Springer. https://doi.org/10.1007/978-3-030-32236-6_51

This work is licensed under a Creative Commons Attribution 4.0 International License.