AI-Based Information Systems
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EDITORIAL
AI-Based Information Systems Peter Buxmann • Thomas Hess • Jason Bennett Thatcher
The Author(s) 2020
1 Introduction Artificial intelligence (AI) is about to bring fundamental changes in our society and economy, touching on how organizations make decisions, deliver services, and evaluate opportunities. Given the breadth of their potential reach across companies of different sizes and in different industries, Erik Brynjolfsson and Andrew McAfee of MIT even speak of AI as ‘‘the most important general-purpose technology of our era’’ (Brynjolfsson and McAfee 2017, p. 2). Today, AI applications in most of the cases are based upon machine learning algorithms, whereby supervised learning, in particular, has become established in practice. Consistent with this optimistic view, leaders in practice predict the widespread use of AI technologies. Forbes, for example, conducted a study among more than 300 executives. 95 percent of the Forbes study’s participants believe that AI will play an essential role in their companies in the future (Forbes Insights Team 2018). The McKinsey Global Institute (MGI) study predicts that AI’s application in companies will result in a global value-added contribution of USD 13 trillion by 2030 (Bughin et al. 2018). P. Buxmann (&) Wirtschaftsinformatik | Software and Digital Business Group, TU Darmstadt, Darmstadt, Germany e-mail: [email protected] T. Hess Institut fu¨r Wirtschaftsinformatik und Neue Medien, LudwigMaximilians-Universita¨t Mu¨nchen, Munich, Germany e-mail: [email protected] J. B. Thatcher Department of Management Information Systems, Temple University, Philadelphia, PA, USA e-mail: [email protected]
AI is already being woven into common applications (Buxmann and Schmidt 2021). For example, AI applications that use machine learning algorithms are used to enable essential firm activities, such as analysis of financial credits, to determine the status of production machines, to support essential services, such as law enforcement, and to protect personal data through cybersecurity. Moreover, AI’s application in health care research – e.g., identifying possible treatment plans or use in drug discovery – have assumed even greater importance as scientists search for treatment and vaccinations in COVID-19-times. From an economic perspective, AI holds the potential to help people, businesses, and governments to lower costs of service delivery and speed up the time required to make decisions. In many cases, algorithms also make faster, more systematic, evidence-based decisions than humans. On the other hand, costs and speed are not the only considerations relevant to decisions. There is a need for a broader conversation about the ethical aspects of decisionmaking and using AI to make decisions that affect people’s lives. These concerns about the potential risks posed to fairness, non-discrimination, transparency and privacy merit attention from policymakers, business leaders, and academic scientists. Of course, no one can tell for sure whether these optimistic
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