INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT

Dr. Aadam Quraishi MD · Shajeni Justin · Ismail Keshta · Dr. Haewon Byeon

Jan 2024 · Xoffencerpublication

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268

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About this ebook

The capacity to understand and have trust in the results generated by models is one of the distinguishing characteristics of high-quality scientific research. Because of the significant impact that models and the outcomes of modeling will have on both our work and our personal lives, it is imperative that we have a solid understanding of models and have faith in the results of modeling. This is something that should be kept in mind by analysts, engineers, physicians, researchers, and scientists in general. Many years ago, picking a model that was transparent to human practitioners or customers often meant selecting basic data sources and simpler model forms such as linear models, single decision trees, or business rule systems. This was the case since selecting a model that was transparent required less processing power. This was the situation as a result of the fact that picking a model that was transparent to human practitioners or customers in general entailed picking a model. Even though these more easy approaches were typically the best option, and even though they continue to be the best option today, they are subject to failure in real-world circumstances in which the phenomena being replicated are nonlinear, uncommon or weak, or very distinctive to particular individuals. Despite the fact that they continue to be the best option, they are sensitive to failure in these kinds of scenarios. The conventional trade-off that existed between the precision of prediction models and the simplicity with which they could be interpreted has been abolished; nevertheless, it is likely that this trade-off was never truly required in the first place. There are technologies that are now accessible that can be used to develop modeling systems that are accurate and sophisticated, based on heterogeneous data and techniques for machine learning, and that can also aid human comprehension of and

About the author

Dr. Aadam Quraishi MD., MBA has research and development roles involving some combination of NLP, deep learning, reinforcement learning, computer vision, and predictive modeling. He is actively leading a team of data scientists, ML researchers, and engineers, conducting research across the full machine learning life cycle - data access, infrastructure, model R&D, systems design, and deployment.

Shajeni Justin is working as an Assistant Professor in the Department of Computer Science at the Siena College of Professional Studies, affiliated with Mahatma Gandhi University. Shajeni Justin earned her undergraduate Degree in Mathematics from St. Teresa’s College, Mahatma Gandhi University and Masters in Computer Application from SSM Engineering College, Anna University, she is pursuing her Ph.D. program in Karapagam Academy of Higher Education, Coimbatore Tamil Nadu. Shajeni Justin received a patent for the Title of Invention as Deep Learning Based Approach to Predict the Pros and Cons of IOT, ML, and Blockchain in Next Generation Industry Environment. She has also presented various academic as well as research-based papers at several national and international conferences.

Ismail Keshta received his B.Sc. and the M.Sc. degrees in computer engineering and his Ph.D. in computer science and engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, in 2009, 2011, and 2016, respectively. He was a lecturer in the Computer Engineering Department of KFUPM from 2012 to 2016. Prior to that, in 2011, he was a lecturer in Princess NourahbintAbdulrahman University and Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia. He is currently an assistant professor in the computer science and information systems department of AlMaarefa University, Riyadh, Saudi Arabia. His research interests include software process improvement, modeling, and intelligent systems.

Dr. Haewon Byeon received the Dr. Sc degree in Biomedical Science from Ajou University School of Medicine. Haewon Byeon currently works at the Department of Medical Big Data, Inje University. His recent interests focus on health promotion, AImedicine, and biostatistics. He is currently a member of international committee for a Frontiers in Psychiatry, and an editorial board for World Journal of Psychiatry. Also, He were worked on 4 projects (Principal Investigator) from the Ministry of Education, the Korea Research Foundation, and the Ministry of Health and Welfare. Byeon has published more than 343 articles and 19 books.

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