Ethical AI in Enterprise Analytics: Balancing Innovation with Fairness, Privacy, and Transparency
DOI:
https://doi.org/10.22399/ijcesen.3762Keywords:
Ethical AI, Enterprise Analytics, Fairness in Machine Learning, Privacy Preservation, AI Transparency, AI Governance, Algorithmic Bias, Responsible AI, Trustworthy AI, Data EthicsAbstract
The proliferation of artificial intelligence (AI) into an enterprise setting has resulted in immense innovation, efficiency, and competitive edge opportunities. Nevertheless, it has also brought about some stern moral issues, especially of fairness, privacy, and transparency. With the increasing role of AI systems in decision-making, organizations come under increased pressure in terms of addressing the problem of algorithmic bias, misuse of data, and the lack of transparency in AI-related outcomes. This review looks at how ethical AI principles are relevant to enterprise analytics, and what key issues arise, as well as synthesizing both empirical and theoretical knowledge. It presents an extensive ethical AI framework with enablers of the organizations, core ethical elements, and enterprise performance that can be measured. Based on changing technologies and stakeholder expectations, the model undergoes a continuous feedback mechanism to ensure that it evolves to support the changing needs. The experimental proof shows a positive correlation between ethical AI practices and stakeholder trust, which proves the business value of ethical implementation in this case. The conclusion of the paper recommends interdisciplinary solutions, automated ethical review, and universally comprehensive guidelines to ensure that AI technological applications can be successfully deployed to enterprises in a responsible way.
References
1. Whittlestone, J., Nyrup, R., Alexandrova, A., & Cave, S. (2019, January). The role and limits of principles in AI ethics: Towards a focus on tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 195-200). DOI: https://doi.org/10.1145/3306618.3314289
2. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019, January). Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency (pp. 59-68). DOI: https://doi.org/10.1145/3287560.3287598
3. Veale, M., & Zuiderveen Borgesius, F. (2021). Demystifying the Draft EU Artificial Intelligence Act—Analysing the good, the bad, and the unclear elements of the proposed approach. Computer Law Review International, 22(4), 97-112. DOI: https://doi.org/10.9785/cri-2021-220402
4. Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. new media & society, 20(3), 973-989. DOI: https://doi.org/10.1177/1461444816676645
5. Shokri, R., & Shmatikov, V. (2015, October). Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security (pp. 1310-1321). DOI: https://doi.org/10.1145/2810103.2813687
6. Winfield, A. F., Michael, K., Pitt, J., & Evers, V. (2019). Machine ethics: The design and governance of ethical AI and autonomous systems [scanning the issue]. Proceedings of the IEEE, 107(3), 509-517. DOI: https://doi.org/10.1109/JPROC.2019.2900622
7. Madaio, M. A., Stark, L., Wortman Vaughan, J., & Wallach, H. (2020, April). Co-designing checklists to understand organizational challenges and opportunities around fairness in AI. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-14). DOI: https://doi.org/10.1145/3313831.3376445
8. Bélisle-Pipon, J. C., Monteferrante, E., Roy, M. C., & Couture, V. (2023). Artificial intelligence ethics has a black box problem. AI & society, 38(4), 1507-1522. DOI: https://doi.org/10.1007/s00146-021-01380-0
9. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. DOI: https://doi.org/10.1177/2053951716679679
10. Flores, A. W., Bechtel, K., & Lowenkamp, C. T. (2016). False positives, false negatives, and false analyses: A rejoinder to machine bias: There's software used across the country to predict future criminals. and it's biased against blacks. Fed. Probation, 80, 38.
11. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big data & society, 3(1), 2053951715622512. DOI: https://doi.org/10.1177/2053951715622512
12. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016, October). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security (pp. 308-318). DOI: https://doi.org/10.1145/2976749.2978318
13. Feenstra, R. A., Delgado López-Cózar, E., & Pallarés-Domínguez, D. (2021). Research misconduct in the fields of ethics and philosophy: researchers’ perceptions in Spain. Science and engineering ethics, 27(1), 1. DOI: https://doi.org/10.1007/s11948-021-00278-w
14. Pandey, A., & Caliskan, A. (2021, July). Disparate impact of artificial intelligence bias in ridehailing economy's price discrimination algorithms. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 822-833). DOI: https://doi.org/10.1145/3461702.3462561
15. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature machine intelligence, 1(9), 389-399. DOI: https://doi.org/10.1038/s42256-019-0088-2
16. Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M. (2021). Ethical machine learning in healthcare. Annual review of biomedical data science, 4(1), 123-144. DOI: https://doi.org/10.1146/annurev-biodatasci-092820-114757
17. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92. DOI: https://doi.org/10.1145/3458723
18. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., ... & Gebru, T. (2019, January). Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency (pp. 220-229). DOI: https://doi.org/10.1145/3287560.3287596
19. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and machines, 28(4), 689-707. DOI: https://doi.org/10.1007/s11023-018-9482-5
20. Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT press.
21. Bountouridis, D., Harambam, J., Makhortykh, M., Marrero, M., Tintarev, N., & Hauff, C. (2019, January). Siren: A simulation framework for understanding the effects of recommender systems in online news environments. In Proceedings of the conference on fairness, accountability, and transparency (pp. 150-159). DOI: https://doi.org/10.1145/3287560.3287583
22. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and trends® in theoretical computer science, 9(3–4), 211-407. DOI: https://doi.org/10.1561/0400000042
23. Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018, October). Explaining explanations: An overview of interpretability of machine learning. In 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA) (pp. 80-89). IEEE. DOI: https://doi.org/10.1109/DSAA.2018.00018
24. Raji, I. D., & Buolamwini, J. (2019, January). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 429-435). DOI: https://doi.org/10.1145/3306618.3314244
25. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
26. Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and discrimination: converting critical concerns into productive inquiry, 22(2014), 4349-4357.
27. Ammanath, B. (2022). Trustworthy AI: a business guide for navigating trust and ethics in AI. John Wiley & Sons.
28. Veale, M., & Binns, R. (2017). Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society, 4(2), 2053951717743530. DOI: https://doi.org/10.1177/2053951717743530
29. Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020). Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center Research Publication, (2020-1). DOI: https://doi.org/10.2139/ssrn.3518482
30. Holland, S., Hosny, A., Newman, S., Joseph, J., & Chmielinski, K. (2020). The dataset nutrition label. Data protection and privacy, 12(12), 1. DOI: https://doi.org/10.5040/9781509932771.ch-001
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