Bias Mitigation in AI Technology

Bias Mitigation in AI Technology

Authors

  • Kruti Pratikkumar Kotak

Keywords:

Artificial Intelligence, Bias Mitigation, Fairness, Ethics, Discrimination, Decision-making, AI Technology, Bias Challenges, Bias Mitigation Approaches

Abstract

Artificial Intelligence (AI) is revolutionizing the way decisions and predictions are made across various domains, from business and finance to government services and healthcare. Its potential to enhance efficiency, productivity, and economic growth is evident, with PwC research estimating that AI could contribute $15.7 trillion to the global economy by 2030. However, the widespread adoption of AI also poses significant challenges, most notably the issue of bias. This research paper delves into the critical topic of bias mitigation in AI technology. It explores the implications of AI bias on decision-making, the limitations of technical solutions, and the broader strategies needed to address bias, making a case for the proactive involvement of business leaders and governance in shaping AI's future. This paper deals a comprehensive analysis of the challenges associated with bias in AI technology and highlight the critical role of business leaders and governance in shaping the future of AI. It will also underscore the importance of moving beyond technical solutions to address the broader dimensions of bias, fostering an equitable and inclusive AI-driven world.

Downloads

Download data is not yet available.

References

• Bach, A. K. P., Norgaard, T. M., Brok, J. C., & van Berkel, N. (2023, April). “If I Had All the Time in the World”: Ophthalmologists’ Perceptions of Anchoring Bias Mitigation in Clinical AI Support. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems: 1-14.

• Cranfield, J. A., Eales, J. S., Hertel, T. W., & Preckel, P. V. (2003). Model selection when estimating and predicting consumer demands using international, cross section data. Empirical economics, 28(2), 353–364

• Derman-Sparks, L. (2016). Guide for selecting anti-bias children’s books. Teaching for Change Books.

• Gonzales, R. M. D., & Hargreaves, C. A. (2022). How can we use artificial intelligence for stock recommendation and risk management? A proposed decision support system. International Journal of Information Management Data Insights, 2(2), Article 100130.

• Harmon, P., & King, D. (1985). Expert systems: Artificial intelligence in business. John Wiley & Sons, Inc..

• Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50.

• Leavy, S., O'Sullivan, B., & Siapera, E. (2020). Data, power and bias in artificial intelligence. arXiv preprint arXiv:2008.07341.

• Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2021). Impact of artificial intelligence on employees working in industry 4.0 led organizations. International Journal of Manpower.

• Nilsson, N. J. (1982). Principles of artificial intelligence. Springer Science & Business Media.

• Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022). Towards a standard for identifying and managing bias in artificial intelligence. NIST special publication, 1270(10.6028).

• Smith, Genevieve and Ishita Rustagi (2020). Mitigating Bias in Artificial Intelligence: An Equity Fluent Leadership Playbook. Berkeley: Haas School of Business, University of California

• Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., ... & Chaspari, T. (2023). A call to action on assessing and mitigating bias in artificial intelligence applications for mental health. Perspectives on Psychological Science, 18(5), 1062-1096.

• Varsha P.S. (2023). How can we manage biases in artificial intelligence systems – A systematic literature review. International Journal of Information Management Data Insights 3 (2023) 100165

• Winston, P. H. (1992). Artificial intelligence. Addison-Wesley Longman Publishing Co., Inc.

Additional Files

Published

30-10-2023

How to Cite

Kruti Pratikkumar Kotak. (2023). Bias Mitigation in AI Technology . Vidhyayana - An International Multidisciplinary Peer-Reviewed E-Journal - ISSN 2454-8596, 9(si1). Retrieved from https://vidhyayanaejournal.org/journal/article/view/1450
Loading...