Trustworthy artificial intelligence
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INVITED PAPER
Trustworthy artificial intelligence Scott Thiebes 1 & Sebastian Lins 1 & Ali Sunyaev 1 Received: 13 May 2020 / Accepted: 9 September 2020 # The Author(s) 2020
Abstract Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI. Keywords Trustworthy artificial intelligence . Artificial intelligence . Trust . Framework . Distributed ledger technology . Blockchain JEL classification M15 O30 A13 C80
Introduction Artificial intelligence (AI) enables computers to execute tasks that are easy for people to perform but difficult to describe formally (Pandl et al. 2020). It is one of the most-discussed technology trends in research and practice today, and estimated to deliver an additional global economic output of around USD 13 trillion by the year 2030 (Bughin et al. 2018). Although AI has been around and researched for decades, it is especially the recent advances in the subfields of machine
Responsible Editor: Rainer Alt * Ali Sunyaev [email protected] Scott Thiebes [email protected] Sebastian Lins [email protected] 1
Department of Economics and Management, Karlsruhe Institute of Technology, Institute AIFB - Building 05.20, KIT-Campus South, 76128 Karlsruhe, Germany
learning and deep learning that not only result in manifold opportunities to contribute to the wellbeing of individuals as well as the prosperity and advancement of organizations and societies but, also in a variety of novel ethical, legal, and social challenges that may severely impede AI’s value contributions, if not handled appropriately (Floridi 2019; Floridi et al. 2018). Examples of issues that are associated with the rapid development and proliferation of AI are manifold. They range from risks of infringing individuals’ privacy (e.g., swapping people’s faces in images or videos via DeepFakes (Turton and Martin 2020) or involuntarily tracking individuals over the Internet via the Clearview AI (Hill 2020)), or the presence of racial bias in widely used AI-based systems (Obermeyer et al. 2019), to the rapid and uncontrolled creation of economic losses via autonomous trading agents (e.g., the loss of millions o
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