Critics currently argue that applied ethics approaches to artificial intelligence (AI) are too principles-oriented and entail a theory–practice gap. Several applied ethical approaches try to prevent such a gap by conceptually translating ethical theory into practice. In this article, we explore how the currently most prominent approaches of AI ethics translate ethics into practice. Therefore, we examine three approaches to applied AI ethics: the embedded ethics approach, the ethically aligned approach, and the Value Sensitive Design (VSD) approach. We analyze each (...) of these three approaches by asking how they understand and conceptualize theory and practice. We outline the conceptual strengths as well as their shortcomings: an embedded ethics approach is context-oriented but risks being biased by it; ethically aligned approaches are principles-oriented but lack justification theories to deal with trade-offs between competing principles; and the interdisciplinary Value Sensitive Design approach is based on stakeholder values but needs linkage to political, legal, or social governance aspects. Against this background, we develop a meta-framework for applied AI ethics conceptions with three dimensions. Based on critical theory, we suggest these dimensions as starting points to critically reflect on the conceptualization of theory and practice. We claim, first, that the inclusion of the dimension of affects and emotions in the ethical decision-making process stimulates reflections on vulnerabilities, experiences of disregard, and marginalization already within the AI development process. Second, we derive from our analysis that considering the dimension of justifying normative background theories provides both standards and criteria as well as guidance for prioritizing or evaluating competing principles in cases of conflict. Third, we argue that reflecting the governance dimension in ethical decision-making is an important factor to reveal power structures as well as to realize ethical AI and its application because this dimension seeks to combine social, legal, technical, and political concerns. This meta-framework can thus serve as a reflective tool for understanding, mapping, and assessing the theory–practice conceptualizations within AI ethics approaches to address and overcome their blind spots. (shrink)

Intelligent algorithms together with various machine learning techniques hold a dominant position among major challenges for contemporary value sensitive design. Self-learning capabilities of current AI applications blur the causal link between programmer and computer behavior. This creates a vital challenge for the design, development and implementation of digital technologies nowadays. This paper seeks to provide an account of this challenge. The main question that shapes the current analysis is the following: What conceptual tools can be developed within the value sensitive (...) design school of thought for evaluating machine learning algorithms where the causal relation between designers and the behavior of their computer systems has been eroded? The answer to this question will be provided through two levels of investigation within the value sensitive design methodology. The first level is conceptual. Within the conceptual level, we will introduce the notion of computer intentionality and will show how this term may be used for solving an issue of non-causal relation between designer and computer system. The second level of investigation is technical. At this level the emphasis will be given to machine learning algorithms. (shrink)