Considerations for development and use of AI in response to COVID-19

Abstract Artificial intelligence (AI) is playing a key supporting role in the fight against COVID-19 and perhaps will contribute to solutions quicker than we would otherwise achieve in many fields and applications. Since the outbreak of the pandemic, there has been an upsurge in the exploration and use of AI, and other data analytic tools, in a multitude of areas. This paper addresses some of the many considerations for managing the development and deployment of AI applications, including planning; unpredictable, unexpected, or biased results; repurposing; the importance of data; and diversity in AI team membership. We provide implications for research and for practice, according to each of the considerations. Finally we conclude that we need to plan and carefully consider the issues associated with the development and use of AI as we look for quick solutions.

[1]  Wim Naudé,et al.  Artificial Intelligence Against Covid-19: An Early Review , 2020, SSRN Electronic Journal.

[2]  Stuart J. Russell,et al.  Research Priorities for Robust and Beneficial Artificial Intelligence , 2015, AI Mag..

[3]  Jeroen van den Hoven,et al.  Meaningful Human Control over Autonomous Systems: A Philosophical Account , 2018, Front. Robot. AI.

[4]  Leandro Soriano Marcolino,et al.  Multi-Agent Team Formation: Diversity Beats Strength? , 2013, IJCAI.

[5]  Eric S. Rosengren,et al.  Board of Governors of the Federal Reserve System , 2002 .

[6]  Yogesh Kumar Dwivedi,et al.  Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda , 2019, Int. J. Inf. Manag..

[7]  Jan C. Weyerer,et al.  Garbage In, Garbage Out: The Vicious Cycle of AI-Based Discrimination in the Public Sector , 2019 .

[8]  Kym Mawson-Lee.  The Online Effect: Transitioning from the Legacy Help Desk to the Online Task Management System , 2006, J. Cases Inf. Technol..

[9]  Stuart E. Madnick,et al.  Overview and Framework for Data and Information Quality Research , 2009, JDIQ.

[10]  Jeanna Neefe Matthews,et al.  Managing Bias in AI , 2019, WWW.

[11]  Tadhg Nagle,et al.  Assessing data quality: A managerial call to action , 2020, Business Horizons.

[12]  Z. Fayad,et al.  Artificial intelligence–enabled rapid diagnosis of patients with COVID-19 , 2020, Nature Medicine.

[13]  Vinton G. Cerf,et al.  Implications of the COVID-19 pandemic , 2020, Commun. ACM.

[14]  Christopher T. Lowenkamp,et al.  False Positives, False Negatives, and False Analyses: A Rejoinder to "Machine Bias: There's Software Used across the Country to Predict Future Criminals. and It's Biased against Blacks" , 2016 .

[15]  Joseph Weiss,et al.  Ethical Implications of Bias in Machine Learning , 2018, HICSS.

[16]  Wim Naudé,et al.  Artificial intelligence vs COVID-19: limitations, constraints and pitfalls , 2020, AI & SOCIETY.

[17]  K. Crawford.  Artificial Intelligence's White Guy Problem , 2016 .

[18]  Rajiv Sabherwal,et al.  How Does Strategic Alignment Affect Firm Performance? The Roles of Information Technology Investment and Environmental Uncertainty , 2019, MIS Q..

[19]  Joshua M. Mueller,et al.  The ABCs of Assured Autonomy , 2019, 2019 IEEE International Symposium on Technology and Society (ISTAS).

[20]  A. Tsouros,et al.  Overview and framework. , 2007 .