Ethical Considerations in Autonomous Decision-Making Systems: A Machine Learning Perspective

Prof. Rajan Verma

Abstract


Autonomous decision-making systems powered by machine learning algorithms have increasingly permeated various domains, raising significant ethical concerns. This paper delves into the ethical implications inherent in the deployment of such systems, primarily focusing on their decision-making capabilities. Through a machine learning perspective, it scrutinizes the challenges of bias, fairness, transparency, and accountability. It explores the intricate balance between optimizing system performance and ensuring ethical considerations in algorithmic decisions. Furthermore, the abstract evaluates existing frameworks and proposes strategies for mitigating ethical risks in autonomous decision-making systems, emphasizing the imperative need for ethical guidelines and regulatory frameworks to safeguard against potential societal, legal, and moral repercussions. This study contributes to the discourse on ethical AI by highlighting the complexities and imperative considerations required for fostering responsible and ethically sound autonomous decision-making systems in a rapidly evolving technological landscape.

References


Whig, P., Bhatia, B., Bhatia, A.B., Sharma, P. (2023). Renewable Energy Optimization System Using Fuzzy Logic. In: Dulhare, U.N., Houssein, E.H. (eds) Machine Learning and Metaheuristics: Methods and Analysis. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-6645-5_8

Peddireddy, K. (2023, October 20). Effective Usage of Machine Learning in Aero Engine test data using IoT based data driven predictive analysis. IJARCCE, 12(10). https://doi.org/10.17148/ijarcce.2023.121003

Mallikarjunaradhya, V., & Pothukuchi, A. S. (2020). Leveraging AI for Predictive Migration Planning and Automated Data Transfer: Ensuring Optimal Cloud Resource Allocation and Data Integrity. Asian Journal of Multidisciplinary Research & Review, 1(2), 77-89.

Whig, P., Sharma, P., Nadikattu, R.R., Bhatia, A.B., Alkali, Y.J. (2023). GAN for Augmenting Cardiac MRI Segmentation. In: Solanki, A., Naved, M. (eds) GANs for Data Augmentation in Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-43205-7_12

Peddireddy, A., & Peddireddy, K. (2023, March 30). Next-Gen CRM Sales and Lead Generation with AI. International Journal of Computer Trends and Technology, 71(3), 21–26. https://doi.org/10.14445/22312803/ijctt-v71i3p104

Mallikarjunaradhya, V., & Pothukuchi, A. S. (2015). The Future of SAAS Startups: How AI Accelerates Market Research and Product Development. Asian Journal of Multidisciplinary Research & Review, 2(4), 444-450.

Whig, P., Sharma, P., Nadikattu, R.R., Bhatia, A.B., Alkali, Y.J. (2023). GAN for Augmenting Cardiac MRI Segmentation. In: Solanki, A., Naved, M. (eds) GANs for Data Augmentation in Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-43205-7_12

Peddireddy, K. (2023, May 18). Kafka-based Architecture in Building Data Lakes for Real-time Data Streams. International Journal of Computer Applications, 185(9), 1–3. https://doi.org/10.5120/ijca2023922740

Pothukuchi, A. S., & Mallikarjunaradhya, V. (2023) COMPREHENSIVE ANALYSIS OF APPLICATIONS, CHALLENGES AND FUTURE PROSPECTS OF AI IN HEALTHCARE 5(8)

Whig, P., Velu, A., Nadikattu, R. R., & Alkali, Y. J. (2024). Role of AI and IoT in Intelligent Transportation. In Artificial Intelligence for Future Intelligent Transportation (pp. 199-220). Apple Academic Press.

Peddireddy, K. (2023, May 11). Streamlining Enterprise Data Processing, Reporting and Realtime Alerting using Apache Kafka. 2023 11th International Symposium on Digital Forensics and Security (ISDFS). https://doi.org/10.1109/isdfs58141.2023.10131800


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 International Journal of Sustainable Development in Computing Science

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

A Double-Blind Peer Reviewed Journal