Artificial Intelligence Ethics Taxonomy- Robotic Process Automation (RPA) as Business Case

Abstract

A Robotic Process Automation (RPA) enabled by Artificial intelligence (AI) has become an important field within the digitalisation of the economy. AI-driven robots and machines are forecasted to grow dramatically in the next years. AI-enabled RPA replaces the work a human would normally do by mimicking interactions with applications and provides direct access to systems using APIs. RPA has superior advantages versus human execution:24x7 execution, eternal lifetime and scalability. Process automatization is per se not a brand-new technology, however due to notable progress in AI, which RPAleverages, it has become an own solution category. RPA enables algorithmic rules without being biased. Ethical considerations intend to make AI-driven RPA more human and introduce morality into the machine learning. The Uber-Waymo trial made transparent how much AI development is influenced by human irrationality and irrational exuberances. It reveals a culture of agile software development, which prioritize releasing the latest software over testing and verification, and one that encourages shortcuts and irrationality. This also give proof that applying AI cannot ensure that irrational exuberances disappear. The reason for this irrational exuberance may have its roots in the exponential growth in computing and storage technologies predicted by Gordon Moore five decades ago. This paper develops a concept how irrational exuberances with the business case of RPA can be prevented from happening. One general approach for solutioning of the issue is to increase transparency. The paper recommends applying technology to make data more accessible and more readable on the application of artificial intelligence. With the aim of application of “transparency technology XBRL (eXtensible Business Reporting Language)” is incorporated. XBRL is part of the choice architecture on regulation by governments (Sunstein, 2013). XBRL is connected to a taxonomy. The paper develops a taxonomy for RPA to make application of artificial intelligence more transparent to the public and incorporates ethical considerations. As a business case the strongly growing RPA industry is selected. The paper focus on the way to enhance AI that aligns with human values. How can incentive be provided that AI systems themselves do not become potential objects of moral concern. The main outcome of the paper is that AI-enabled RPA reveal moral concerns however transparency technologies at the same time also offer way to mitigate such risks.