Unveiling the Pandora's Box: A Multifaceted Exploration of Ethical Considerations in Generative AI for Financial Services and Healthcare
Keywords:
Generative AI, Financial Services, HealthcareAbstract
AI may alter industries. Generative AI may enhance healthcare and financial services choices, procedures, and user experiences. These advancements raise ethical issues. The ethical concerns of generative AI in financial services and healthcare are examined in this tough study.
Generative AI analyzes sensitive financial, medical, and other information. Data storage and use endanger privacy and security. Securing sensitive data from unwanted access, breaches, and abuse requires good security. Strong data governance systems increase user trust and openness, whereas anonymization and differential privacy decrease them.
Generative AI taught on biased datasets may increase inequality. AI-powered financial services may discriminate against some groups when evaluating loan applications or investments. Healthcare apps may misdiagnose and treat. To reduce biases, use different training datasets, fairness indicators in model development, and human supervision.
References
Aggarwal, A., et al. (2022, June 20). On the Algorithmic Justice League's Principles for a Fairer Algorithm: A Call to Action for the ML Community. [arXiv:2009.05402] on arXiv arxiv.org
Brundage, M., et al. (2018, December). The Montreal Declaration for Responsible AI.
Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New York: NYU Press.
Diakopoulou, E., et al. (2020, August 07). Fairness in Explainable AI. [arXiv:2008.04303] on arXiv arxiv.org
Doshi-Velez, F., & Kim, B. (2017). Towards a Rigorous Science of Interpretable Machine Learning. Proceedings of the 2017 ACM Conference on Knowledge Discovery and Data Mining (KDD '17). Association for Computing Machinery, New York, NY, USA, 801-809. [DOI: 10.1145/3097945.3097998]
Goodman, B., & Flaxman, S. (2017, August 03). European regulators crack down on opaque algorithms. Nature News, 547(7665), 603-604. [DOI: 10.1038/547.603a]
Jobin, A., et al. (2019). The public interest in algorithmic decision-making. Nature Machine Intelligence, 1(11), 799-801. [DOI: 10.1038/s41586-019-1881-9]
Manyika, M., et al. (2017). A human-centered approach to AI. McKinsey Global Institute.
Mehrabi, M., et al. (2019). A multiverse analysis of skin tones in AI fairness. ACM Conference on Fairness, Accountability, and Transparency (pp. 349-360). Association for Computing Machinery. [DOI: 1.1145/3351272.3351275]
Mittelstadt, B., et al. (2019). Algorithmic Bias in Computer Vision: Findings and Recommendations from the Fairness, Accountability, and Transparency Conference. FAT* '19: Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 1659-1663). Association for Computing Machinery. [DOI: 10.1145/3351272.3351314]
Obermeyer, Z., et al. (2019). Discrepancy by design: A case study of algorithmic bias in healthcare. Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 1219-1228). Association for Computing Machinery. [DOI: 10.1145/3351272.3351310]
Rajkomar, A., et al. (2018). Ensuring Fairness in Artificial Intelligence Scoring Systems for Risk Assessment in Health Care. New England Journal of Medicine, 378(22), 2146-2153. [DOI: 10.1056/NEJMsa1801743]
Rudin, C., et al. (2019). The machine learning lottery: Surrogate validation of performance and fairness under distribution shift. Proceedings of the 36th International Conference on Machine Learning (pp. 8777-8786). PMLR.
Selbst, A. D., et al. (2019). Fairness and Abstraction in Sociotechnical Systems. Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59-68). Association for Computing Machinery. [DOI: 10.1145/3351272.3351277]
Shin, J., et al. (2019). A roadmap for using machine learning in healthcare. Nature Biotechnology.