CIO Panel on Ethical Framework for AI & Big Data
The CIO panel on Ethical Framework for Big Data was organized as part of the 14th Annual Midwest Association for Information Systems 2019 Conference (MWAIS) chaired by Professor Donald Heath and hosted by the College of Business, UW-Oshkosh. The CIO panel was organized and moderated by Professor Gaurav Bansal of Cofrin School of Business, UW-Green Bay.
The panel offered a forum for discussion of the issues on the development and adoption of an ethical framework for AI. The panel included the following four CIOs – Mr. Murali Balakrishnan, CIO at TIDI Products; Mr. David Cagigal, CIO for the State of Wisconsin; Mr. Raman Mehta, CIO for Visteon Corporation; and Mr. Todd Thiel, VP-IT at SECURA Insurance Companies.
The composition of the panel allowed for a variety of perspectives to be shared from multiple domains – government, industry, and academia. Panelists’ provided deep insight on issues pertaining to the usage, adoption, and challenges associated with AI, along with the pathway going forward. The panel also provided an open dialogue between panelists and the audience.
Despite its importance and benefits, AI poses many challenges. Understanding how AI can be rightfully integrated with the business needs, and social objectives will only help in the growth and adoption of AI technologies. Therefore, the challenges associated with the use of AI need deeper examination and understanding.
There are plenty of examples of unfairness perpetrated by unchecked usage of AI in enterprises – which Cathy O’ Neil terms as “Weapons of Math Destruction” (Weapons of Math Destruction by Cathy O’ Neil, Broadway Books, ISBN: 978-0553418835) and Virginia Eubanks equates with “Automating Inequality” (Automating Inequality by Virginia Eubanks, St. Martin’s Press, ISBN: 978-1250074317). The unchecked deployment of algorithms in the consumerization of insights actioned from AI and Big Data is increasing efficiency on the one hand but is also magnifying the inaccuracies and unfairness that existed before these systems were designed and implemented on the other.
It is important to analyze how these algorithms exaggerate human biases pertaining to motivated reasoning and confirmation biases, among others. The panel was organized to reflect on the issues around the following: usage and adoption of AI, future of AI, challenges of AI, and recommended solutions.
The panel helped uncover the common issues across different industries. The group reflected on the forces that are driving the widespread adoption of AI (competitive pressures, efficiency gains, data-driven insights, augmentation, regulatory structure), but also highlighted the concerns in adopting AI (skill miss-match, need to “tech up” the workforce, resistance due to loss of autonomy, opaque algorithms among others).
There are also concerns with the technology itself, such as lurking biases – as one of the panelists mentioned algorithms are human biases written in the form of mathematical formulas. The panel also discussed an initial idea for an ethical framework for AI briefly outlining the who, what, why, when, where, and how questions. The panel also reflected on the forces that can enable or constraint the adoption of such a framework.
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