TOWARDS RESPONSIBLE AI: THE INTERSECTION OF TECHNOLOGY, SOCIETY, AND GOVERNMENT LAW
DOI:
https://doi.org/10.60087/jklst.vol3.n3.p340-354Abstract
The rapid development and deployment of Artificial Intelligence (AI) technologies are transforming various aspects of society, raising both unprecedented opportunities and challenges. As AI increasingly integrates into critical sectors such as healthcare, finance, and government, questions about ethical responsibility, societal impact, and regulatory oversight have become central. This paper explores the intersection of technology, society, and government law in the quest for responsible AI. We investigate how societal values and ethical frameworks can be embedded into AI systems, the role of government regulations in shaping AI practices, and the necessity for collaborative governance among technology developers, policy-makers, and civil society. Additionally, we address the potential risks of AI, including privacy concerns, biases, and accountability, proposing a comprehensive approach that balances innovation with ethical integrity. Through this interdisciplinary analysis, we aim to provide a foundation for a responsible AI framework that respects societal norms and aligns with regulatory standards, paving the way for equitable and trustworthy AI applications.
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References
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There's software used across the country to predict future criminals. And it's biased against blacks. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
Binns, R. (2018). 'I am not a robot': The ethical implications of AI's growing role in decision-making. Journal of Technology and Ethics, 35(2), 145-160. https://doi.org/10.2139/ssrn.3023439
Bughin, J., Chui, M., & Manyika, J. (2018). Artificial intelligence: The next digital frontier? McKinsey Global Institute.
Burrell, J. (2016). How the machine 'thinks': Understanding opacity in AI systems. Big Data & Society, 3(1), 1-9. https://doi.org/10.1177/2053951715622512
Citron, D. K., & Pasquale, F. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89(1), 1-33. https://doi.org/10.2139/ssrn.2371297
Coeckelbergh, M. (2020). AI ethics: The role of education in understanding the societal impacts of AI. Springer Nature.
Diakopoulos, N. (2016). Accountability in algorithmic decision-making. Communications of the ACM, 59(2), 56-62. https://doi.org/10.1145/2844110
Dastin, J. (2018). Amazon’s AI recruiting tool shows bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Floridi, L., Herkulano-Houzel, F., & Lange, A. (2018). Ethics of artificial intelligence and robotics. Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/ethics-ai/
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present, and future. Seminars in Cancer Biology, 44, 25-36. https://doi.org/10.1016/j.semcancer.2017.04.011
Lee, M. (2018). AI strategies and governance: The case of China. International Journal of Information Management, 38(1), 1-8. https://doi.org/10.1016/j.ijinfomgt.2017.08.004
Lum, K., & Isaac, W. (2016). To predict and serve?. Significance, 13(5), 14-19. https://doi.org/10.1111/j.1740-9713.2016.00960.x
Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. In Turing’s Legacy (pp. 55-79). Springer.
Obermeyer, Z., Powers, B. W., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Price, W. N., & Cohen, I. G. (2019). Privacy in the age of artificial intelligence: A defense of the GDPR. Science, 365(6455), 128-131. https://doi.org/10.1126/science.aaw9862
Richardson, R., Schultz, D., & Crawford, K. (2019). Dirty data, dirty decisions: The ethics of artificial intelligence in criminal justice. Fordham Law Review, 87(5), 1161-1193. https://ir.lawnet.fordham.edu/flr/vol87/iss5/5
Russell, S. J. (2019). Human compatible: Artificial intelligence and the problem of control. Viking.
Susskind, R., & Susskind, D. (2015). The future of the professions: How technology will transform the work of human experts. Oxford University Press.
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
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