TRR 318 - Project B6: Ethics and normativity of explainable AI

Overview

Research on explaining and explainability needs ethical reflection, because explanations can be used to manipulate users or to create acceptance for a technology that is ethically or legally unacceptable. Moreover, designing explainability to meet ethical demands (e.g., justifiability, accountability, autonomy) must not necessarily be in line with meeting users’ interests. The ethical reflection proposed in our project encompasses three intertwined lines of investigation: First, we shall systematically classify the different purposes, needs, and requirements of explanations. Second, our project will also reflect ethically on technological development within TRR 318. Third, we shall combine both lines of research to pursue both a theoretical and a practical goal: On the one hand, we want to extend the TRR 318’s model of explaining as a social practice into an ethical framework of explaining AI. On the other hand, we shall apply this framework to concrete projects within TRR 318 to (a) explicate how current design choices reflect ethical considerations and users’ demands by following the methodological steps of value sensitive design (VSD). From these insights, we shall then (b) formulate concrete design recommendations to inform further development within TRR 318. In the long run, B06 will also enhance the TRR’s consideration of the social contexts, because it identifies issues that cannot be fixed technically but need to be addressed on a social or legal level.

Key Facts

Grant Number:
438445824
Project type:
Forschung
Project duration:
07/2023 - 12/2025
Funded by:
Deutsche Forschungsgemeinschaft (DFG)
Website:
Homepage

More Information

Principal Investigators

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Prof. Dr. Suzana Alpsancar

Transregional Collaborative Research Centre 318

About the person
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Prof. Dr. Tobias Matzner

Transregional Collaborative Research Centre 318

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Publications

Responsibilities in sXAI
K.J. Rohlfing, S. Alpsancar, C. Schulte, in: Social Explainable AI, Springer Nature Singapore, Singapore, 2026, pp. 157–177.
Tasking AI Fairly. How to Empower AI Practitioners With sXAI?
S. Alpsancar, E. Stamboliev, in: Social Explainable AI, Springer Nature Singapore, Singapore, 2026, pp. 557–581.
The Risk of Manipulation and Deception in sXAI
S. Alpsancar, M. Klenk, in: Social Explainable AI, Springer Nature Singapore, Singapore, 2026, pp. 583–616.
Grenzen des Verstehens
M. Philippi, in: 2025.
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