Application of Artificial Intelligence in Research
Keywords:
Research Methodology, Implementation, Investigation, Future ResearchAbstract
The purpose of this article is to present the application of artificial intelligence (AI) in research through the synthesis of various documents and studies. The overall content includes an overview of how AI application in research has significantly transformed the methods and processes of studying and analyzing data. In the digital age, where technology is rapidly advancing, AI has become a crucial tool for enhancing research efficiency, from big data analysis, modeling, and automating processes such as Machine Learning, Natural Language Processing, and Neural Networks. AI also help in generating new knowledge and solving complex problems more effectively. However, the use of AI in research still faces challenges related to ethics and data integrity, such as data privacy, security, bias, and transparency in algorithmic operations. Addressing these challenges is crucial to ensure that AI in research is applied responsibly and benefits society.
References
Agarwal, A., & Dhar, V. (2020). Big data, (AI), and economic opportunity: Implications for policy. Journal of Economic Perspectives, 34(4), 1–28.
Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216–9221. https://doi.org/10.1073/pnas.1804840115
Baker, T., & Smith, L. (2019). Education rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://www.nesta.org.uk/report/education-rebooted/
Binns, R. (2018). Fairness in machine learning: Mitigating bias in AI systems. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. https://dl.acm.org/doi/10.1145/3173574.3173795
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Blumenstock, J. E., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile
phone metadata. Science, 350(6264), 1073–1076. https://doi.org/10.1126/science.aac4420
Broucke, S. V. D., Baesens, B., & Vanthienen, J. (2017). Automated business process discovery. In Business Process Management Workshops (pp. 407–419). Springer.
Bryant, R. E., Katz, R. H., & Lazowska, E. D. (2012). Big-data computing: Creating revolutionary breakthroughs in commerce, science, and society. Computing Community Consortium.
Bryman, A. (2012). Social research methods (4th ed.). Oxford University Press.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2017). Artificial intelligence: The next digital frontier?. McKinsey Global Institute.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209. https://doi.org/10.1007/s11036-013-0489-0
Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2020). Using machine learning and data mining to leverage electronic health record data. Journal of the American Medical Informatics Association, 28(2), 313–324. https://doi.org/10.1093/jamia/ocaa292
Cohen, I. G., Amarasingham, R., Shah, A., Xie, B., & Lo, B. (2019). The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Affairs, 33(7), 1139–1147. https://doi.org/10.1377/hlthaff.2014.0048
Crawford, K. & Paglen, T. (2019). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University.
Dastin, J. (2019). Amazon scrapped a secret (AI) recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
Denscombe, M. (2010). The good research guide: For small-scale social research projects (4th ed.). Open University.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People-An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Gero, J. S. (2021). Artificial intelligence in design research: Research methodology. AI EDAM, 35(1), 5–19.
Gilbert, M. (2020). Social media analytics: A novel method for research in social sciences. International Journal of Social Research Methodology, 23(2), 123–138.
Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist. Open University.
Gilbert, N., Ahrweiler, P., & Pyka, A. (2020). Innovation networks: Simulation and policy. Routledge.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
He, Y., & Zhang, X. (2020). Automated research processes: Trends and challenges in computational social science. Computational Social Networks, 7(1), 1–17.
Hofman, J. M., Sharma, A., Watts, D. J., & Goldstein, D. G. (2021). How you say it matters: Relational framing in communication about AI-based decisions. Nature Communications, 12(1), 1–12.
Hutson, M. (2017). AI researchers allege that machine learning is alchemy. Science, 360(6388), 478–479. https://doi.org/10.1126/science.360.6388.478
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2021). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(3), 230–243.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Jurafsky, D., & Martin, J. H. (2019). Speech and language processing (3rd ed. draft). Pearson.
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures & their consequences. Sage.
Kitchin, R. (2022). The data revolution: A critical analysis of big data, open data and data infrastructures. Sage.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Makridakis, S. (2020). Forecasting, AI, and big data: Challenges and opportunities. Foresight: The International Journal of Applied Forecasting, 55, 36–42.
Manyika, J., Silberg, J., & Presten, D. (2019). What AI can and can’t do (yet) for your business. McKinsey Quarterly.
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
Menzies, T., & Zimmermann, T. (2020). Software analytics: So, what? IEEE Software, 37(1), 64–68.
Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38.
Muller, M., Geyer, W., Soule, T., Daniels, S., & Cheng, L. P. (2021). AI ethics: Global perspectives, challenges, and opportunities. Journal of Artificial Intelligence Research, 70, 465–497. https://doi.org/10.1613/jair.1.12136
O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches (3rd ed.). Sage.
Rajan, S., & Shanbhag, D. N. (2020). Artificial intelligence in scientific research: Applications, challenges, and ethics. Journal of Data Science and Analytics, 2(3), 123–134.
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., & Bengio, Y. (2019). Tackling climate change with machine learning. arXiv. https://arxiv.org/abs/1906.05433
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Sutherland, W. J., & Wood, A. (2017). The need for evidence-based policy in environmental management: A perspective from the social sciences. Journal of Environmental Management, 204, 232–237. https://doi.org/10.1016/j.jenvman.2017.05.037
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
Topol, E. (2019a). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
Topol, E. J. (2019b). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Tsvetkova, M., Yasseri, T., Meyer, M., Pickering, J. B., Engen, V., Walland, P., & Tadic, B. (2017). Understanding human-machine networks: A cross-disciplinary survey. ACM Computing Surveys, 50(1), 1–35.
Witten, I. H., Frank, E., & Hall, M. A. (2017). Data mining: Practical machine learning tools and techniques (4th ed.). Morgan Kaufmann.
Wong, Z. S. Y., Zhou, J., & Zhang, Q. (2019). Artificial intelligence for infectious disease big data analytics and surveillance. Artificial Intelligence in Medicine, 100, 101–108. https://doi.org/10.1016/j.artmed.2019.01.003
Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2020). Dive into deep learning. arXiv. https://arxiv.org/abs/2001.04005
Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., & Telenti, A. (2019). A primer on deep learning in genomics. Nature Genetics, 51(1), 12–18. https://doi.org/10.1038/s41588-018-0295-5
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
