Citation :

Anubhav Seth, 2024. "Exploring the Ethical Issues in Artificial Intelligence: Towards Fairness, Bias and Accountability in AI System" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 2: 8-16.

Abstract :

Artificial intelligence (AI) has the potential to improve healthcare, finance, education, and other fields, but it also creates ethical issues that must be addressed. These days, AI based technologies are frequently used to make decisions that have a significant influence on people and society. Their choices might have an impact on everyone, anywhere, and at any time, raising possible human rights problems. This paper explores the complex ethical environment around AI, to offer a thorough examination of the difficulties and requirements related to ensuring fairness, reducing bias, and promoting accountability in AI systems. A variety of approaches and tools, such as data analysis, algorithm analysis, human analysis, fairness metrics, and context analysis, can be used to solve these ethical issues. It is feasible to create trustworthy, acceptable, and equitable AI systems by using these proactive measures, guaranteeing that AI advances society ethically and responsibly. This article discusses potential directions for future study as well as suggestions for practitioners handling AI and ethics in various fields.

References :

[1] Finlayson, S. G., Subbaswamy, A., Singh, K., Bowers, J., Kupke, A., Zittrain, J., ... & Saria, S. (2021). The clinician and dataset shift in artificial intelligence. New England Journal of Medicine, 385(3), 283-286.

[2] Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337-339.

[3] Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC medical informatics and decision making, 21, 1-23.

[4] Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021). Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25, 1315-1360.

[5] Cui, M., & Zhang, D. Y. (2021). Artificial intelligence and computational pathology. Laboratory Investigation, 101(4), 412-422.

[6] Bullock, J., Luccioni, A., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. (2020). Mapping the landscape of artificial intelligence applications against COVID-19. Journal of Artificial Intelligence Research, 69, 807-845.

[7] Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets, 31, 447-464.

[8] Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330.

[9] Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J. (2020). Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE Access, 8, 220121-220139.

[10] Belk, R. (2021). Ethical issues in service robotics and artificial intelligence. The Service Industries Journal, 41(13-14), 860-876.

[11] Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440.

[12] Brendel, A. B., Mirbabaie, M., Lembcke, T. B., & Hofeditz, L. (2021). Ethical management of artificial intelligence. Sustainability, 13(4), 1974.

[13] Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221-4241.

[14] Zhang, J., Shu, Y., & Yu, H. (2023). Fairness in design: a framework for facilitating ethical artificial intelligence designs. International Journal of Crowd Science, 7(1), 32-39.

[15] Zhang, J., & Zhang, Z. M. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23(1), 7.

[16] Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468.

[17] Deiana, G., Dettori, M., Arghittu, A., Azara, A., Gabutti, G., & Castiglia, P. (2023). Artificial intelligence and public health: evaluating ChatGPT responses to vaccination myths and misconceptions. Vaccines, 11(7), 1217.

[18] Merhi, M. I. (2023). An evaluation of the critical success factors impacting artificial intelligence implementation. International Journal of Information Management, 69, 102545.

[19] Hosseini, M., Resnik, D. B., & Holmes, K. (2023). The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts. Research Ethics, 17470161231180449.

[20] Aquino, Y. S. J. (2023). Making decisions: Bias in artificial intelligence and data driven diagnostic tools. Australian journal of general practice, 52(7), 439-442.

[21] Kuhlberg, J. A., Headen, I., Ballard, E. A., & Martin Jr, D. (2023). Advancing Community Engaged Approaches to Identifying Structural Drivers of Racial Bias in Health Diagnostic Algorithms. arXiv preprint arXiv:2305.13485.

[22] Dakshit, S., Dakshit, S., Khargonkar, N., & Prabhakaran, B. (2023). Bias Analysis in Healthcare Time Series (BAHT) Decision Support Systems from Meta Data. Journal of Healthcare Informatics Research, 1-29.

[23] Drukker, K., Chen, W., Gichoya, J., Gruszauskas, N., Kalpathy-Cramer, J., Koyejo, S., ... & Giger, M. (2023). Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment. Journal of Medical Imaging, 10(6), 061104-061104.

[24] Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 178 (11): 1544–1547.

[25] Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns, 2(10).

[26] Paulus, J. K., & Kent, D. M. (2020). Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities. NPJ digital medicine, 3(1), 99.

[27] Zuiderveen Borgesius, F. J. (2020). Strengthening legal protection against discrimination by algorithms and artificial intelligence. The International Journal of Human Rights, 24(10), 1572-1593.

[28] Caron, M. S. (2019). The transformative effect of AI on the banking industry. Banking & Finance Law Review, 34(2), 169-214.

[29] Malik, N., Kar, A. K., Tripathi, S. N., & Gupta, S. (2023). Exploring the impact of fairness of social bots on user experience. Technological Forecasting and Social Change, 197, 122913.

[30] Howard, A., & Borenstein, J. (2018). The ugly truth about ourselves and our robot creations: the problem of bias and social inequity. Science and engineering ethics, 24, 1521-1536.

[31] Tanna, M., & Dunning, W. (2023). Bias and discrimination in the use of AI in the financial sector. In Artificial Intelligence in Finance (pp. 320-349). Edward Elgar Publishing.

[32] Lee, J. (2020). Access to finance for artificial intelligence regulation in the financial services industry. European Business Organization Law Review, 21, 731-757.

[33] Pamuk, M., Schumann, M., & Nickerson, R. C. (2024). What Do the Regulators Mean? A Taxonomy of Regulatory Principles for the Use of AI in Financial Services. Machine Learning and Knowledge Extraction, 6(1), 143-155.

[34] Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y. S., Kay, J., ... & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074.

[35] Shin, D. (2020). User perceptions of algorithmic decisions in the personalized AI system: Perceptual evaluation of fairness, accountability, transparency, and explainability. Journal of Broadcasting & Electronic Media, 64(4), 541-565.

[36] Porayska-Pomsta, K., & Rajendran, G. (2019). Accountability in human and artificial intelligence decision-making as the basis for diversity and educational inclusion. Artificial Intelligence and Inclusive Education: Speculative Futures and Emerging Practices, 39-59.

[37] Khan, S. (2023). The Ethical Imperative: Addressing Bias and Discrimination in AI-Driven Education. Social Sciences Spectrum, 2(1), 89-96.

[38] Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440.

[39] Baker, R. (2023). Getting Past the Current Trade-Off Between Privacy and Equity in Educational Technology. The Economics of Equity in K-12 Education: Connecting Financial Investments with Effective Programming, 123.

[40] Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223-235.

[41] "DIFFERENTIAL PRIVACY TECHNIQUES IN MACHINE LEARNING FOR ENHANCED PRIVACY PRESERVATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.b148-b153, February-2024, Available: http://www.jetir.org/papers/JETIR2402116.pdf

[42] Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221-4241.

[43] van Nood, R., & Yeomans, C. (2021). Fairness as equal concession: critical remarks on fair AI. Science and Engineering Ethics, 27(6), 73.

[44] John-Mathews, J. M., Cardon, D., & Balagué, C. (2022). From reality to world. A critical perspective on AI fairness. Journal of Business Ethics, 178(4), 945-959.

[45] Varona, D., & Suárez, J. L. (2022). Discrimination, bias, fairness, and trustworthy AI. Applied Sciences, 12(12), 5826.

[46] Robert Jr, L. P., Bansal, G., Melville, N., & Stafford, T. (2020). Introduction to the special issue on AI fairness, trust, and ethics. AIS Transactions on Human-Computer Interaction, 12(4), 172-178.

[47] Akter, S., Dwivedi, Y. K., Biswas, K., Michael, K., Bandara, R. J., & Sajib, S. (2021). Addressing algorithmic bias in AI-driven customer management. Journal of Global Information Management (JGIM), 29(6), 1-27.

[48] Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., ... & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4).

[49] Amann, J., Blasimme, A., Vayena, E., Frey, D., Madai, V. I., & Precise4Q Consortium. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC medical informatics and decision making, 20, 1-9.

[50] Vilone, G., & Longo, L. (2021). Notions of explainability and evaluation approaches for explainable artificial intelligence. Information Fusion, 76, 89-106

Keywords :

Artificial Intelligence, AI Ethical Issues, Fairness, Bias, Accountability.