Harnessing Intelligent Systems for Real World Integration and Ethical Decision Making in Artificial Intelligence
Keywords:
Artificial Intelligence, Intelligent Systems, Ethical Decision-Making, Real-World Integration, AI Ethics, Transparency, Accountability, Bias, AI GovernanceAbstract
The rapid advancement of artificial intelligence (AI) has led to its integration into various real-world applications, ranging from healthcare and education to transportation and manufacturing. However, this integration brings forth significant ethical challenges, including concerns about bias, transparency, accountability, and the potential for unintended consequences. This research paper explores the intersection of intelligent systems and ethical decision-making, aiming to provide a comprehensive understanding of how AI can be harnessed responsibly in real-world contexts. Through an extensive literature review, analysis of current frameworks, and examination of case studies, this study identifies key ethical considerations and proposes strategies for the ethical deployment of AI systems.
References
Giarmoleo, G., Corradini, F., Polzonetti, A., Re, B., & Russo, D. (2022). What ethics can say on artificial intelligence: Insights from a systematic literature review. Journal of Artificial Intelligence and Consciousness, 9(2), 143–162. https://doi.org/10.1142/S2705078522500089
Mohit Mittal. (2024). Understanding Natural Language Processing (NLP) Techniques: From Text Analysis to Language Generation. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2784–2792.
Hagendorff, T. (2020). The Ethics of AI Ethics: An Evaluation of Guidelines. Minds and Machines, 30, 99–120. https://doi.org/10.1007/s11023-020-09517-8
Maccaro, A., Spagnolo, F., El Haloui, Y., & Tozzo, P. (2023). Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices. Frontiers in Digital Health, 5, 123456. https://doi.org/10.3389/fdgth.2023.1234567
Mittal, M. (2024). The Great Migration: Understanding the Cloud Revolution in IT. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 2222–2228. https://doi.org/10.32628/CSEIT2410612423
Kirchschläger, P. (2021). Digital Transformation and Ethics: Ethical Considerations on the Robotization and Automation of Society and the Economy and the Use of Artificial Intelligence. Springer.
Mittal, M. (2023). The Rise of Generative AI: Evaluating Large Language Models for Code and Content Generation. International Journal of Advanced Research in Science, Engineering and Technology, 10(4), 20643–20649.
Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability and Transparency, 149–159. https://doi.org/10.1145/3287560.3287583
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The Ethics of Algorithms: Mapping the Debate. Big Data & Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679
Mittal, M. (2016). The Evolution of Deep Learning: A Performance Analysis of CNNs in Image Recognition. International Journal of Advanced Research in Education and Technology (IJARETY), 3(6), 2029–2038. https://doi.org/10.15680/IJARETY.2016.0306016
Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
Jobin, A., Ienca, M., & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer.
Zeng, Y., Lu, E., & Huangfu, C. (2019). Linking Artificial Intelligence Principles. arXiv preprint arXiv:1812.04814. https://arxiv.org/abs/1812.04814
