Original Study

Open Access

|

Peer-reviewed

The Ethics of Algorithmic Decision-Making: Ensuring Fairness, Transparency, and Accountability in Data Analytics

Rajesh Kamisetty , Raj Nagamangalam

DIP: 18.02.030/20240901

DOI: 10.25215/2455/0901030

Received: February 24, 2024; Revision Received: March 15, 2024; Accepted: March 30, 2024

Abstract

Algorithmic decision-making is a typical replacement enhancing efficiency and innovation which has made its way through the United States’ industries while stirring ethical questions such as implementation of bias, lack of transparency, and accountability. This study thus explores the aforementioned challenges and their effects on different fields including; healthcare, finance and law enforcement. In particular, it showcases specific approaches to the development of fairness-aware algorithms, as well as transparent systems and sound accountability frameworks. The study focuses on AI and policy for ethical practices to foster and develop trust in deployment of AI based systems across industries in the United States.

Keywords

The author(s) appreciates all those who participated in the study and helped to facilitate the research process.

The author(s) declared no conflict of interest.

This is an Open Access Research distributed under the terms of the Creative Commons Attribution License (www.creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any Medium, provided the original work is properly cited.

© 2024, Kamisetty, R. & Nagamangalam, R.

Responding Author Information

Rajesh Kamisetty @ msrprojectshyd@gmail.com

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Article Overview

ISSN 2348-5396

ISSN 2349-3429

DIP: 18.02.030/20240901

DOI: 10.25215/2455/0901030

Published in

Volume 09, Issue 1, January – March, 2024

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  1. Zhou, J., 2022. The Ethical Implementation of Algorithm-based Decision-making: An Analytic Roadmap to Examine Accountability and Responsibility of Designing and Implementing Audit Data Analytics. Journal of Accounting, Ethics & Public Policy, JAEPP, 23(2), pp.309-309.
  2. Nassar, A. and Kamal, M., 2021. Ethical dilemmas in AI-powered decision-making: a deep dive into big data-driven ethical considerations. International Journal of Responsible Artificial Intelligence, 11(8), pp.1-11.
  3. Kordzadeh, N. and Ghasemaghaei, M., 2022. Algorithmic bias: review, synthesis, and future research directions. European Journal of Information Systems, 31(3), pp.388-409.
  4. Kasy, M. and Abebe, R., 2021, March. Fairness, equality, and power in algorithmic decision-making. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 576-586).
  5. Starke, C., Baleis, J., Keller, B. and Marcinkowski, F., 2022. Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society, 9(2), p.20539517221115189.
  6. Cobbe, J., Lee, M.S.A. and Singh, J., 2021, March. Reviewable automated decision-making: A framework for accountable algorithmic systems. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 598-609).
  7. Günther, M. and Kasirzadeh, A., 2022. Algorithmic and human decision making: for a double standard of transparency. AI & SOCIETY, 37(1), pp.375-381.
  8. Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M. and Floridi, L., 2021. The ethics of algorithms: key problems and solutions. Ethics, governance, and policies in artificial intelligence, pp.97-123.
  9. Saveliev, A. and Zhurenkov, D., 2021. Artificial intelligence and social responsibility: the case of the artificial intelligence strategies in the United States, Russia, and China. Kybernetes, 50(3), pp.656-675.
  10. Mökander, J., Juneja, P., Watson, D.S. and Floridi, L., 2022. The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?.Minds and Machines, 32(4), pp.751-758.
  11. Percy, C., Dragicevic, S., Sarkar, S. and d’Avila Garcez, A., 2022. Accountability in AI: From principles to industry-specific accreditation. AI Communications, 34(3), pp.181-196.
  12. de Almeida, P.G.R., dos Santos, C.D. and Farias, J.S., 2021. Artificial intelligence regulation: a framework for governance. Ethics and Information Technology, 23(3), pp.505-525.
  13. Poblet, M., Allen, D.W., Konashevych, O., Lane, A.M. and Diaz Valdivia, C.A., 2020. From Athens to the Blockchain: oracles for digital democracy. Frontiers in Blockchain, 3, p.575662.
  14. Egger, D.J., Gambella, C., Marecek, J., McFaddin, S., Mevissen, M., Raymond, R., Simonetto, A., Woerner, S. and Yndurain, E., 2020. Quantum computing for finance: State-of-the-art and future prospects. IEEE Transactions on Quantum Engineering, 1, pp.1-24.
  15. [15] Felzmann, H., Fosch-Villaronga, E., Lutz, C. and Tamò-Larrieux, A., 2020. Towards transparency by design for artificial intelligence. Science and engineering ethics, 26(6), pp.3333-3361.
  16. Durán, J.M. and Jongsma, K.R., 2021. Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. Journal of Medical Ethics, 47(5), pp.329-335.
  17. Saxena, D., Badillo-Urquiola, K., Wisniewski, P.J. and Guha, S., 2021. A framework of high-stakes algorithmic decision-making for the public sector developed through a case study of child-welfare. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), pp.1-41.
  18. Burrell, J. and Fourcade, M., 2021. The society of algorithms. Annual Review of Sociology, 47(1), pp.213-237.
  19. Choi, J., 2021. Current and Future CONSIDERATIONS for the Use of Artificial Intelligence by the United States’ Department of Defense. Robotics & AI Ethics, 6(1), pp.1-6.
  20. Turchin, A. and Denkenberger, D., 2020. Classification of global catastrophic risks connected with artificial intelligence. Ai & Society, 35(1), pp.147-163.
  21. Grote, T. and Berens, P., 2020. On the ethics of algorithmic decision-making in healthcare. Journal of medical ethics, 46(3), pp.205-211.
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