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AI are not just technical tools but rather 'virtual citizens', endowed with agency and discretion. Whereas their character as a black box is widely discussed, their moral inaccessibility is not. Broadening the issue of 'fairness' recently applied to algorithms, our research concern is as follows: How is the complex, typically ambiguous and contradictory moral infrastructure of late modern societies mirrored and eventually transformed by AI? In order to conceptualize this question, we combine the normative reasoning of the Sociology of Critique (SoC) resp. the Economy of Conventions with a coactive learning approach of AI/ML systems. SoC distinguishes several interwoven orders of justification, assuming that individuals have to justify their everyday doings and decisions with regard to competing moral orders. Fairness, transparency, accountability and trust in this view do not constitute fixed entities or system properties, but have to be continually negotiated and mediated referring to legitimate registers of justification. Transferring this approach to AI as 'virtual citizens' (1) learning machines will be confronted with conflicting moral orders and justifications. To ask how society is being translated into learning machines is to observe how plural orders of justification are actively adapted by AI systems. For this purpose, 'spaces of negotiation' will be created in which AI and humans interact based on a plurality of orders of worth.

• Prof. Dr. Uwe Vormbusch

  Fernuniversität in Hagen
  Fakultät für Kultur- und Sozialwissenschaften
  Institut für Soziologie
  Hagen
  

• Dr. Carlos Castillo

  Universitat Pompeu Fabra
  Department of Information and Communication
  Technology
  Barcelona
  Spanien
  

• Prof. Dr. Stefan Smolnik

  Fernuniversität in Hagen
  Fakultät für Wirtschaftswissenschaft
  BWL, insb. Betriebliche Anwendungssysteme
  Hagen
  

• Prof. Dr. Kristian Kersting

  Technische Universität Darmstadt
  Fachbereich 20, Informatik
  Compute Science Department
  & Center for Cognitive Science
  FG Maschinelles Lernen
  Darmstadt