Fairness, Accountability, Transparency, and Explainability in Intelligent Automation : Emerging Trends in Internet Technologies, Summer Term 2021

Background: The focus of this thesis is to find out which properties are important for autonomous vehicles to generate trust. Reference is made to the concept of FATE properties (Fairness, Accountability, Transparency, and Explainability) by Shin (2020).
Objective: The aim is to identify the extent to which such a FATE framework helps build trust, the extent to which the FATE attributes are already being applied in practice, and the measures that can be used to promote the FATE attributes.
Methods: As part of the research, six experts from research and industry were interviewed in qualitative, semi-structured interviews. For the qualitative analysis of the interviews, the Inductive and the Deductive-Inductive Hybrid Approach of Mayring is used.
Results: The results show that individual FATE properties are considered in the development, but that the framework in this constellation is not known to the experts and is not applied in this composition. Rather, the terms are often used in an undifferentiated manner. In principle, the experts see the potential for application in the FATE framework. Especially since compliance with each aspect promotes trust.
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Conclusion: It is important here not only to pay attention to the AI and the algorithms behind it but to apply the framework to the entire automotive development. In addition, through the analysis of the interviews, several measures could be identified that promote compliance with the individual FATE properties and thus foster trust in the autonomous vehicle.