Ethics and Artificial Intelligence

Authors

  • Bhautik Modi Department of Community and Family Medicine, All India Institute of Medical Sciences, Rajkot, Gujarat, India

DOI:

https://doi.org/10.55489/ijmr.1303202582

Keywords:

Artificial Intelligence, Ethics, Algorithmic Bias, Privacy, Accountability, Innovation

Abstract

Artificial Intelligence (AI) is revolutionizing industries ranging from healthcare and finance to transportation and warfare. However, its rapid advancement presents profound ethical challenges, including algorithmic bias, privacy concerns, accountability, and workforce disruption. This article explores key dimensions of AI ethics, such as defining ethical boundaries, addressing bias, and navigating privacy in the age of data-driven innovation. It highlights ethical dilemmas in healthcare, workforce implications, and military applications of AI. Furthermore, the article underscores the need for global regulatory frameworks, interdisciplinary collaboration, and stakeholder engagement to ensure responsible AI development. As AI continues to evolve, a balance between innovation and ethical oversight is paramount to aligning technological progress with societal values and human rights. By fostering inclusivity and prioritizing transparency, we can navigate the complexities of AI ethics and harness its transformative potential responsibly.

References

1. Floridi L. Ethics of artificial intelligence: The future of AI. Nat Mach Intell. 2020;2(5):237-239. DOI: https://doi.org/10.1038/s42256-020-0180-7

2. Binns R. Fairness in machine learning: Lessons from political philosophy. Proc Conf Fairness Accountability Transpar. 2018;1(1):149-159. DOI: https://doi.org/10.1145/3287560.3287565 PMCid:PMC6545736

3. Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nat Mach Intell. 2019;1(9):389-399. DOI: https://doi.org/10.1038/s42256-019-0088-2

4. Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. The ethics of algorithms: Mapping the debate. Big Data Soc. 2016;3(2):1-21. DOI: https://doi.org/10.1177/2053951716679679

5. Noble SU. Algorithms of oppression: How search engines reinforce racism. New York University Press; 2018.

6. Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Comput Surv. 2021;54(6):1-35. DOI: https://doi.org/10.1145/3457607

7. Barocas S, Hardt M, Narayanan A. Fairness and machine learning. Cambridge University Press; 2019.

8. Zuboff S. The age of surveillance capitalism. PublicAffairs; 2019.

9. Regulation (EU) 2016/679 of the European Parliament and of the Council. Gen Data Prot Regul. 2016. Available from: https://gdpr-info.eu/.

10. Dwork C, Roth A. The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci. 2014;9(3-4):211-407. DOI: https://doi.org/10.1561/0400000042

11. Bryson JJ, Theodorou A. How society can maintain human-centric artificial intelligence. IEEE Comput. 2019;52(7):31-39.

12. Wachter S, Mittelstadt B, Floridi L. Transparent, explainable, and accountable AI for robotics. Sci Robot. 2017;2(6):eaan6080. DOI: https://doi.org/10.1126/scirobotics.aan6080 PMid:33157874

13. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. DOI: https://doi.org/10.1038/s41591-018-0300-7 PMid:30617339

14. Obermeyer Z, Emanuel EJ. Predicting the future-big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216-1219. DOI: https://doi.org/10.1056/NEJMp1606181 PMid:27682033 PMCid:PMC5070532

15. Frey CB, Osborne MA. The future of employment: How susceptible are jobs to computerisation? Technol Forecast Soc Change. 2017;114:254-280. DOI: https://doi.org/10.1016/j.techfore.2016.08.019

16. Acemoglu D, Restrepo P. Artificial intelligence, automation, and work. NBER Work Pap Ser. 2018;24196. DOI: https://doi.org/10.3386/w24196 PMid:30441167

17. European Commission. Proposal for a regulation laying down harmonized rules on artificial intelligence (Artificial Intelligence Act). 2021. Available from: https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence-artificial-intelligence.

18. OECD. Principles on Artificial Intelligence. Organisation for Economic Co-operation and Development; 2019. Available from: https://www.oecd.org/going-digital/ai/principles/.

19. Ryan M, Stahl BC. Artificial intelligence ethics guidelines for developers and users: Clarifying their content and normative implications. J Inf Commun Ethics Soc. 2020;18(2):161-178. DOI: https://doi.org/10.1108/JICES-12-2019-0138

20. Cath C, Wachter S, Mittelstadt B, Taddeo M, Floridi L. Artificial intelligence and the 'good society': The US, EU, and UK approach. Sci Eng Ethics. 2018;24(5):505-528. DOI: https://doi.org/10.1007/s11948-017-9901-7

21. Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: Addressing ethical challenges. PLoS Med. 2018;15(11):e1002689. DOI: https://doi.org/10.1371/journal.pmed.1002689 PMid:30399149 PMCid:PMC6219763

Downloads

Published

2025-07-01

How to Cite

Modi, B. (2025). Ethics and Artificial Intelligence. International Journal of Medical Research, 13(03), 61–65. https://doi.org/10.55489/ijmr.1303202582

Issue

Section

Perspective