Ethical Implications of AI in Decision-Making Processes
Keywords:
ethics, ai, decision-making, bias, transparencyAbstract
This article explores the ethical considerations associated with the deployment of artificial intelligence in decision-making systems. We examine case studies from various sectors including healthcare, finance, and law, addressing issues such as bias, accountability, and transparency. Our research proposes guidelines for ethical AI development to ensure fairness and protect stakeholders' rights. We emphasize the importance of interdisciplinary dialogue in forming ethical standards for AI technologies.
References
Satyanarayana, D., & Elmirghani, J. M. (2010, December). An energy efficient network architecture for infrastructured wireless networks. In 2010 IEEE Global Telecommunications Conference GLOBECOM 2010 (pp. 1-6). IEEE.
Satyanarayana, D., & Elmirghani, J. M. (2009, September). A voronoi based energy efficient architecture for wireless networks. In 2009 Third International Conference on Next Generation Mobile Applications, Services and Technologies (pp. 377-382). IEEE.
Satyanarayana, D., & Rao, S. V. (2008, July). Local Delaunay triangulation for mobile nodes. In 2008 First International Conference on Emerging Trends in Engineering and Technology (pp. 282-287). IEEE.
Satyanarayana, D., & Alasmi, A. S. (2022, October). Detection and mitigation of DDOS based attacks using machine learning algorithm. In 2022 International Conference on Cyber Resilience (ICCR) (pp. 1-5). IEEE.
Satyanarayana, D., & Rao, S. V. (2007, December). Fault tolerant local Delaunay triangulation for ad hoc sensor networks. In 2007 Third International Conference on Wireless Communication and Sensor Networks (pp. 36-40). IEEE.
Alkalbani, A. S., Mantoro, T., & Degala, S. (2016, June). Energy-Distance Aware Clustering Scheme (E-DACS) for Wireless Sensor Networks. In Recent Advances in Information and Communication Technology 2016: Proceedings of the 12th International Conference on Computing and Information Technology (IC2IT) (pp. 277-292). Cham: Springer International Publishing.
Kumar, N., & Kataria, V. Enhanced Sentiment Classification using a Multi-layered Stacked Ensemble Architecture.
Rahimov, E. R. (2010). BASE PRINCIPAL OF MANAGING OF NETWORK SOFTWARE SECURITY BY VULNERABILITIES DETERMINATION MODEL. Computer Sciences and Telecommunications, (5), 70-74.
