How can emerging technologies such as AI and ML be leveraged to advance social good, and what are the broad areas of opportunities and challenges associated with their development and deployment, based on current research?

Ayush  Chauhan

doi:10.18535/ijecs/v12i09.4755

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Ayush Chauhan,

Article Date Published : 20 September 2023 | Page No.: 25809-25833 |

DOI https://doi.org/10.18535/ijecs/v12i09.4755

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Abstract

The research paper focuses on two crucial aspects: Artificial Intelligence (AI) and Machine Learning (ML). These emerging technologies have significant implications for social welfare. AI involves developing computer systems that can perform tasks requiring human intelligence, while ML enables machines to learn from data and improve their performance without explicit programming. AI and ML are crucial because they have the potential to revolutionize industries, automate tasks, enhance decision-making, and bring about social benefits in areas like healthcare, finance, education, and the tech industry that rely heavily on AI and ML (Margetts, 2022; Auernhammer, 2020). The primary research inquiry explores the intricate ways these technologies can be harnessed to propel social progress, all while considering the broader spectrum of opportunities and challenges entailed in their development and implementation, drawing upon existing research. The second research question focuses explicitly on the design of ChatGPT—an eminent AI application—and examines how it embodies human values such as honesty, integrity, and peace and its social responsiveness concerning diversity, inclusivity, equity, and ethical considerations.

The initial segment of the research focuses on unraveling the potential contributions of AI and ML to social welfare. By undertaking an extensive review and synthesis of current literature, this paper identifies the manifold applications of these technologies across diverse domains, encompassing healthcare, education, environmental sustainability, and social justice. This comprehensive analysis sheds light on the potential benefits, such as enhanced decision-making, heightened efficiency, and augmented accessibility while also recognizing the associated challenges like algorithmic bias, privacy concerns, and ethical deliberations (Zajko, 2021). By delving into the current state of research, this paper aims to provide an encompassing comprehension of the opportunities and challenges arising from the development and utilization of AI and ML technologies for the betterment of society.

The subsequent portion of the research places particular emphasis on ChatGPT—an influential AI application—and endeavors to examine its design in relation to human values and social responsiveness. This paper delves into how the system embodies values such as honesty, integrity, and peace. Moreover, this study examines the system's inclusivity, diversity, equity, and ethical considerations, evaluating its responsiveness to the prompts provided, its capacity to avoid biased responses, and adherence to ethical guidelines. This analysis strives to offer insights into the strengths and limitations of ChatGPT's design and to foster a comprehensive understanding of its impact on societal values and social interactions.

The research findings significantly contribute to the burgeoning field of AI and ML for social welfare by illuminating these technologies' potential benefits and challenges. Additionally, the analysis of ChatGPT's design provides valuable insights for AI developers and policymakers, empowering them to cultivate responsible AI systems that align with human values and address societal needs effectively.

References

Siau, K., & Wang, W. (2020). Artificial intelligence (AI) ethics: ethics of AI and ethical AI. Journal of Database Management (JDM), 31(2), 74–87.

Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson Education.

McCorduck, P. (2004). Machines who think: A journey through the history of artificial intelligence. MIT Press.

McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Magazine, 27(4), 12.

Shortliffe, E. H., Buchanan, B. G., & Feigenbaum, E. A. (1975). MYCIN: A rule-based computer program for medical diagnosis. Computers and Biomedical Research, 8(3), 356-379.

Crevier, D. (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks.

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson Education Limited.

Russell, S. J., & Norvig, P. (2003). Artificial Intelligence: A Modern Approach. 3rd ed. Upper Saddle River, NJ: Prentice Hall.

Muggleton, S. (2014). Alan Turing and the development of Artificial Intelligence. AI Communications, 27(1), 3-10.

Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.

National Academies of Sciences, Engineering, and Medicine. (2020). Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.

Brynjolfsson, E., & Chui, M. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York: W. W. Norton & Company.

Mittelstadt, B., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2019). The ethics of artificial intelligence. Nature Machine Intelligence, 1(1), 20-24.

OpenAI. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation.

Arrieta, A. B., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2019). Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. ArXiv. /abs/1910.10045

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of black-box models. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1145-1154). ACM.

Nilsson, N. J. (2010). The quest for artificial intelligence. Cambridge University Press.

IBM. (n.d.). Watson Health. https://www.ibm.com/watson/health/

Khan Academy. (n.d.). https://www.khanacademy.org/

Kiva. (n.d.). Retrieved from https://www.kiva.org/

Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). A definition, benchmark and database of AI for social good initiatives. Nature Machine Intelligence, 3(2), 111-115.

Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to Design AI for Social Good: Seven Essential Factors. Science and Engineering Ethics, 26(3), 1771-1796.

Latonero, M. (2019). Opinion: AI For Good Is Often Bad. Wired.

Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., et al. (2020). AI for social good: unlocking the opportunity for positive impact. Nature Communications, 11(1), 2468.

Bondi, E., Xu, L., Acosta-Navas, D., & Killian, J. A. (2021). Envisioning communities: a participatory approach towards AI for social good. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 425-436).

Fernandez-Luque, L., & Imran, M. (2018). Humanitarian health computing using artificial intelligence and social media: A narrative literature review. International journal of medical informatics, 114, 136-142.

Green, B. P. (2020). Artificial intelligence and ethics: Sixteen challenges and opportunities. Markkula Center for Applied Ethics at Santa Clara University. https://www.scu.edu/ethics/all-about-ethics/artificial-intelligence-and-ethics-sixteen-challenges-and-opportunities

Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99-120.

Castelfranchi, C. (1998). Modelling social action for AI agents. Artificial Intelligence, 103(1-2), 157-182.

Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751-752.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., et al. (2018). AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and Machines, 28, 689-707.

Auernhammer, J. (2020). Human-centered AI: The role of Human-centered Design Research in the development of AI.

Chowdhury, N. A., & Rahman, S. (2023). A brief review of ChatGPT: Limitations, Challenges and Ethical-Social Implications.

Enterprise DNA. (n.d.). What Is Chat GPT? – Everything You Need to Know.

Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International.

OpenAI. (2022, November 30). Introducing ChatGPT.

Vakkuri, V., Kemell, K.-K., Kultanen, J. and Abrahamsson, P. 2020. The current state of industrial practice in artificial intelligence ethics. Ieee Software. 37, 4 (2020), 50–57.

Astobiza, A. M., Toboso, M., Aparicio, M., & López, D. (2021). AI ethics for sustainable development goals. IEEE Technology and Society Magazine, 40(2), 66-71.

Morley, J., Machado, C. C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: a mapping review. Social Science & Medicine, 260, 113172.

Schiff, D., Rakova, B., Ayesh, A., Fanti, A., & Lennon, M. (2021). Explaining the principles to practices gap in AI. IEEE Technology and Society Magazine, 40(2), 81-94.

LaChat, M.R. (1986). Artificial intelligence and ethics: an exercise in the moral imagination. Ai Magazine, 7(2), 70–70.

Whittlestone, J., Nyrup, R., Alexandrova, A., & Cave, S. (2019). The role and limits of principles in AI ethics: towards a focus on tensions. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 195–200.

Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1, 61–65.

Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56.

Basu, Satabdi & Biswas, Gautam & Kinnebrew, John. (2017). Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment. User Modeling and User-Adapted Interaction. 26. 10.1007/s11257-017-9187-0.

Shepley A, Falzon G, Meek P, Kwan P. Automated location invariant animal detection in camera trap images using publicly available data sources. Ecol Evol. 2021 Mar 10;11(9):4494-4506. doi: 10.1002/ece3.7344. PMID: 33976825; PMCID: PMC8093655.

Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183-186.

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.

Buolamwini, J. & Gebru, T.. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, in Proceedings of Machine Learning Research 81:77-91

Auernhammer, J. (2020). Human-centered AI: The role of Human-centered Design Research in the development of AI.

Margetts, H. (2022). Rethinking AI for good governance. Daedalus, 151(2), 360-371.

Zajko, M. (2021). Conservative AI and social inequality: conceptualizing alternatives to bias through social theory. AI & Society, 36(3), 1047-1056.

Berendt, B. (2019). AI for the Common Good?! Pitfalls, challenges, and ethics pen-testing. Paladyn, Journal of Behavioral Robotics, 10(1), 44-65.

Chevaleyre, Y., Endriss, U., Lang, J., & Maudet, N. (2008). Preference handling in combinatorial domains: From AI to social choice. AI magazine, 29(4), 37.

Coeckelbergh, M. (2010). Health care, capabilities, and AI assistive technologies. Ethical theory and moral practice, 13, 181-190.

Fernandez-Luque, L., & Imran, M. (2018). Humanitarian health computing using artificial intelligence and social media: A narrative literature review. International journal of medical informatics, 114, 136-142.

Imran, M., Ofli, F., Caragea, D., & Torralba, A. (2020). Using AI and social media multimodal content for disaster response and management: Opportunities, challenges, and future directions. Information Processing & Management, 57(5), 102261.

Larsson, S. (2020). On the governance of artificial intelligence through ethics guidelines. Asian Journal of Law and Society, 7(3), 437–451.

Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: how may AI and GPT impact academia and libraries? Library Hi Tech News.

Moore, J. (2019). AI for not bad. Frontiers in Big Data, 2, 32.

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International Journal of Engineering and Computer Science, 2023.

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Article Details

Issue: Vol. 12 No. 09 (2023)

Page No.: 25809-25833

Section: Articles

DOI: https://doi.org/10.18535/ijecs/v12i09.4755

How to Cite

Chauhan, A. . (2023). How can emerging technologies such as AI and ML be leveraged to advance social good, and what are the broad areas of opportunities and challenges associated with their development and deployment, based on current research?. International Journal of Engineering and Computer Science, 12(09), 25809–25833. https://doi.org/10.18535/ijecs/v12i09.4755

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How can emerging technologies such as AI and ML be leveraged to advance social good, and what are the broad areas of opportunities and challenges associated with their development and deployment, based on current research?

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