The Role of AI and Machine Learning in Automating Tasks and Decision-Making
DOI:
https://doi.org/10.36676/jrps.v16.i1.42Keywords:
AI, Machine Learning, task automation, decision-making, predictive analytics, real-time decision-making, personalized healthcare, fraud detection, smart cities, autonomous vehicles, explainability,Abstract
The rapid developments in Artificial Intelligence (AI) and Machine Learning (ML) in the past decade have transformed numerous industries extensively through optimization and automation of decision-making. From manufacturing and medicine to finance and urban development, AI and ML technologies have proved themselves capable of streamlining operations, optimizing functional efficiency, and reducing human intervention. But despite the ubiquity of AI and ML, the field of research on achieving the full potential of the technologies, especially in real-time decision-making, interpretability, and ethics, still remains very large. Recent studies have shown that AI can automate repetitive tasks, from input of data, fraud detection, and customer service, to playing a role in more complex decision-making situations, from predictive finance analytics to personalized healthcare. But the gap lies in the balance between AI-automated functions and human input, especially where risky cases are involved, such as in medicine and autonomous vehicles. Moreover, issues with explaining AI models limit their large-scale deployment in sensitive decision-making areas.
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• Brynjolfsson, E., & McAfee, A. (2016). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
• 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
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. A., Kaiser, Ł., & Polosukhin, I. (2020). Attention is all you need. NeurIPS, 30. https://arxiv.org/abs/1706.03762
• Wang, D., L. B. White, & Zheng, Y. (2018). Machine learning in finance: A review. Journal of Financial Data Science, 1(1), 55-75. https://doi.org/10.3934/jfds.2018.1.55
• Feng, S., Yang, W., & Seneviratne, A. (2019). Reinforcement learning in high-frequency trading: An empirical study. Journal of Computational Finance, 23(2), 1-28. https://doi.org/10.21314/JCF.2019.045
• Avasarala, V., Lee, J., & Yadav, R. (2016). Robotic process automation in the digital era: Optimizing business processes in organizations. Business Process Management Journal, 22(5), 1-14. https://doi.org/10.1108/BPMJ-11-2015-0224
• Roth, H. R., Farag, A., & Henschke, C. I. (2019). Machine learning in medical decision support systems. Journal of Clinical Decision Support, 13(2), 12-24. https://doi.org/10.1002/jcds.2929
• Choi, S., Choi, Y., & Cho, J. (2020). AI and supply chain optimization: A deep learning approach for demand forecasting and inventory management. Journal of Supply Chain Management, 56(3), 57-72. https://doi.org/10.1111/jscm.12275
• Searle, R., Lee, T., & Patil, S. (2016). AI and human resource management: Automating recruitment and employee performance evaluation. International Journal of Human Resource Management, 27(9), 986-1005. https://doi.org/10.1080/09585192.2016.1216590
• Bennett, L., Brinson, M., & Harris, T. (2017). AI-driven decision support systems in disaster response: A case study on flood prediction. Disaster Management and Humanitarian Response Journal, 5(2), 23-36. https://doi.org/10.1016/j.dhr.2017.07.004
• Goodall, N. J. (2014). Machine ethics and autonomous vehicles. In Road Vehicle Automation (pp. 93-102). Springer Vieweg, Berlin. https://doi.org/10.1007/978-3-319-05990-7_10
• Baur, D., F. T. Lee, & Thomson, M. (2019). AI and climate change: Using machine learning for environmental monitoring and sustainability. Journal of Environmental Informatics, 31(2), 35-48. https://doi.org/10.2133/jei.32.123
• Kitchin, R. (2020). The ethics of smart cities and the challenges of AI governance. Urban Studies, 57(7), 1451-1471. https://doi.org/10.1177/0042098019852347
• Chaffey, D. (2016). Artificial intelligence in marketing: Leveraging machine learning to optimize customer engagement. Journal of Digital Marketing, 15(2), 112-126. https://doi.org/10.1080/17523272.2016.1124268
• Gentsch, P. (2019). Predictive analytics and AI for customer engagement. Journal of Customer Strategy, 11(1), 1-17. https://doi.org/10.1007/s11002-019-0734-4
• Lin, P., Abney, K., & Bekey, G. A. (2023). Autonomous vehicles and the ethics of AI decision-making. AI & Ethics, 3(4), 263-275. https://doi.org/10.1007/s43681-023-00034-3
• Jouppi, N., E. H. Hsu, & Sahni, A. (2024). AI in smart cities: Automating urban decision-making with machine learning. Urban Computing, 22(1), 1-17. https://doi.org/10.1145/3361568
• Gao, S., Zhang, M., & Liao, J. (2021). Machine learning in environmental management: Addressing climate change through predictive modeling. Environmental Modelling & Software, 145, 105195. https://doi.org/10.1016/j.envsoft.2021.105195
• Shalev-Shwartz, S., Shai, N., & S. Levi (2017). Deep learning for self-driving cars: An overview of recent advances. IEEE Transactions on Intelligent Transportation Systems, 18(7), 2369-2382. https://doi.org/10.1109/TITS.2017.2767000
• Miller, T. (2022). Explainable artificial intelligence in medical decision support systems. Journal of Artificial Intelligence in Medicine, 132, 101289. https://doi.org/10.1016/j.artmed.2022.101289
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