Experienced IT practitioners know that errors will occur. A big part of building and managing complex systems is dealing with risk management (which includes identification and mitigation strategies). This is hard enough when documentation and source code exist. But the current state of ML-based AI tends to result in opaque black boxes, which make this activity, um, challenging. David Biros, Madhav Sharma, and Jacob Biros explore the implications for organizations and their processes.
Article
Vulnerability and Risk Mitigation in AI and Machine Learning
Posted October 10, 2019 | Leadership | Technology | Amplify
About The Author
David Biros is Associate Professor of Management Science and Information Systems and Fleming Chair of Information Technology Management at Oklahoma State University. A retired Lieutenant Colonel of the US Air Force, Dr. Biros’s last assignment was as Chief Information Assurance Officer for the AF-CIO. His research interests include deception detection, insider threat, information system trust, and ethics in information technology. Dr. Biros has… Read More
Madhav Sharma is a PhD student, studying management science and information systems at Oklahoma State University. His research interests include diffusion of innovation, use and implication of artificial intelligence, machine learning, and the Internet of Things. Mr. Sharma earned a master’s degree in telecom management and an MBA, both from Oklahoma State University. He can be reached at madhav.sharma@okstate.edu.
Jacob Biros is a mechanical engineer and data analyst at Chura Data in Okinawa, Japan. He is an experienced system developer who has worked on a wide variety of artificial intelligence/machine learning–related projects ranging from natural language processing to dynamic pricing. Mr. Biros earned a bachelor of engineering degree from Oklahoma State University. He can be reached at jakebiros@gmail.com.
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