Navigating The Labyrinth: A Comprehensive Review Of Emerging Artificial Intelligence Technologies, Ethical Considerations, And Global Governance Models In The Pursuit Of Trustworthy AI

Authors

  • Rajiv Avacharmal AI/ML Risk Lead, Independent Researcher, USA Author
  • Leeladhar Gudala Software Engineering Masters, Deloitte Consulting, Pennsylvania, USA Author
  • Srinivasan Venkataramanan Senior Software Developer – American Tower Corporation, Woburn, Massachusetts, USA Author

Keywords:

Artificial Intelligence, AI Ethics, Emerging Technologies

Abstract

The rapid rise of AI has altered many aspects of life. AI might improve face recognition, autonomous automobiles, and algorithmic healthcare. Fast AI growth requires ethical AI development and deployment studies. This article addresses worldwide AI ethics trends and potential AI technology ethical risks. 

New tech and AI ethics The research examines Deep Learning, NLP, and Generative AI. Brain-inspired Deep Learning classifies and detects patterns in photos. Their "black box" opaqueness limits explanation and accountability. NLP is helping machines understand and produce human language. Data biases in training cause bias and ethics failures. AI language forms are realistic and creative, creating ethical concerns. Protection against modified video/audio deepfakes is required.

References

B. Marr, “Artificial intelligence in practice: How businesses are using AI to achieve digital transformation,” John Wiley & Sons, 2019.

A. Jobin, O. Perrone, and S. Vayena, “The ethics of artificial intelligence,” Nature, vol. 563, no. 7733, pp. 513-518, 2018.

OECD, “OECD principles on artificial intelligence,” OECD Publishing, Paris, 2019, [Online]. Available: [invalid URL removed]

European Commission, “Ethics guidelines for trustworthy AI,” 2019, [Online]. Available: [invalid URL removed]

I. A. Glover and P. M. Grant, “Digital Communications,” 3rd ed. Harlow: Prentice Hall, 2009.

C. W. Li and G. J. Zhang, “A survey on deep learning techniques for speech processing,” China Communications, vol. 14, no. 2, pp. 1-17, 2017.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436-444, 2015.

J. Eisenstein, “Introduction to natural language processing,” MIT press, 2019.

T. Bäckström and J. Liang, “Natural language processing for social media,” Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1-180, 2012.

A. Radford, A. Odena, S. Ilya Sutskever, L. Vincent, and I. Goodfellow, “Generative pre-training from transformers,” in Proceedings of the 36th International Conference on Machine Learning, pp. 4212-4221, 2019.

M. A. Friedberg, “A critical analysis of deepfakes: A threat to democracy?,” Fordham L. Rev., vol. 87, no. 5, pp. 2073-2120, 2020.

S. Rudin, C. Fong, and M. Breneman, “Provable interpretability of machine learning models,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1063-1070, 2018.

A. Doshi-Velez and M. T. ({b}), “Information complexity and algorithmic transparency,” in Proceedings of the 38th International Conference on Machine Learning, vol. 131, pp. 1789-1797, 2021.

S. Spiekermann, P. Flach, and A. B. Goldberg, “A taxonomy of privacy,” in International Conference on Data and Knowledge Engineering, pp. 567-574, Springer, 2001.

A. Machanavajjhala, D. Kifer, J. M. Abowd, J. P. Hughes, and M. Byres, “Incognito: Privacy-preserving data publishing,” in Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 493-504, 2009.

H. Tschofenig, A. M. Shahbaz, A. E. Hassan, and M. U. Sajjad, “Federated learning with differential privacy: A systematic review,” arXiv preprint arXiv:2007.13459, 2020.

B. Mittelstadt, P. Wachter, and M. L. Floridi, “Transparency in artificial intelligence,” Nature Medicine, vol. 25, no. 1, pp. 105-110, 2019.

Downloads

Published

11-10-2023

How to Cite

[1]
Rajiv Avacharmal, Leeladhar Gudala, and Srinivasan Venkataramanan, “Navigating The Labyrinth: A Comprehensive Review Of Emerging Artificial Intelligence Technologies, Ethical Considerations, And Global Governance Models In The Pursuit Of Trustworthy AI”, Aus. J. of Machine Learning Res. & App., vol. 3, no. 2, pp. 331–347, Oct. 2023, Accessed: Mar. 14, 2025. [Online]. Available: https://ajmlra.org/index.php/publication/article/view/79