Ethical Implications of AI in Decision-Making Processes: A Review

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Prof. Madhu Singh

Abstract

The increasing integration of AI in decision-making processes raises significant ethical concerns. This paper reviews the ethical implications of AI in various sectors, including healthcare, finance, and law enforcement. We analyze issues related to bias, transparency, and accountability, providing a comprehensive overview of the challenges and proposing frameworks for ethical AI deployment.

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Ethical Implications of AI in Decision-Making Processes: A Review. (2023). Research-Gate Journal, 9(9). https://research-gate.in/index.php/Rgj/article/view/31
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How to Cite

Ethical Implications of AI in Decision-Making Processes: A Review. (2023). Research-Gate Journal, 9(9). https://research-gate.in/index.php/Rgj/article/view/31

References

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. https://doi.org/10.48550/arXiv.1810.04805

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778. https://doi.org/10.1109/CVPR.2016.90

Nadella, G. S., Gonaygunta, H., Meduri, K., & Satish, S. (2023). Adversarial Attacks on Deep Neural Network: Developing Robust Models Against Evasion Technique. Transactions on Latest Trends in Artificial Intelligence, 4(4)

Meduri, K., Nadella, G. S., Gonaygunta, H., & Meduri, S. S. (2023). Developing a Fog Computing-based AI Framework for Real-time Traffic Management and Optimization. International Journal of Sustainable Development in Computing Science, 5(4), 1-24.

Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531. https://doi.org/10.48550/arXiv.1503.02531

Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.1412.6980

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

Li, Y., & Zhou, H. (2020). A survey on deep learning in medical image analysis. Medical Image Analysis, 64, 101766. https://doi.org/10.1016/j.media.2020.101766

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), 4765-4774. https://doi.org/10.5555/3295222.3295230

Gonaygunta, H., Meduri, S. S., Podicheti, S., & Nadella, G. S. (2023). The Impact of Virtual Reality on Social Interaction and Relationship via Statistical Analysis. International Journal of Machine Learning for Sustainable Development, 5(2), 1-20.

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236

Rajpurkar, P., Irvin, J., Zhu, K., K_classifier, K., & Nguyen, D. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. Proceedings of the 30th Conference on Neural Information Processing Systems (NeurIPS), 1-11. https://doi.org/10.48550/arXiv.1711.05225

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