Selected publications

Tolmeijer, S., Christen, M., Kandul, S., Kneer, M. and Bernstein, A. (2022, April). Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making. In CHI Conference on Human Factors in Computing Systems (pp. 1-17).

Tolmeijer, S., Zierau, N., Janson, A., Wahdatehagh, J. S., Leimeister, J. M. M., & Bernstein, A. (2021, May). Female by Default?–Exploring the Effect of Voice Assistant Gender and Pitch on Trait and Trust Attribution. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-7).

Bernstein, A., Vreese, C. D., Helberger, N., Schulz, W., Zweig, K., Baden, C., … & Zueger, T. (2021). Diversity in News RecommendationDagstuhl Manifestos9(1), 43-61.

Tolmeijer, S., Weiss, A., Hanheide, M., Lindner, F., Powers, T. M., Dixon, C., & Tielman, M. L. (2020, March). Taxonomy of Trust-Relevant Failures and Mitigation Strategies. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 3-12). (Honorable Mention)

Tolmeijer, S., Kneer, M., Sarasua, C., Christen, M., & Bernstein, A. (2020). Implementations in Machine Ethics: A SurveyACM Computing Surveys (CSUR)53(6), pp.1-38.

More can be found on Google Scholar.


Social influence of artificial intelligence is multifaceted. Across different domains, different influences can be seen or expected in the future. The following topics are of interest:

  • Hybrid Intelligence: while the fear of AI taking over jobs is widely spread, it is unfounded when AI is integrated into society in a constructive way. Humans have different strengths and weaknesses from AI; combining the strengths of both can be done by designing technology in a way to unlock this potential.
  • Machine Ethics: as machines become more autonomous and take more complex decisions, society needs to think about which actions are permittable and preferred when taken by AI, as well as how this can be implemented.
  • Trust in AI: trust is a driving factor in technology use. Specifically, an appropriate level of trust is needed: both overtrust and undertrust of AI can lead to disastrous results. However, what is the ‘right’ level of trust, how is trust formed, which factors influence it, how do we measure it, and how do we influence it?
  • Recommended Information Diversity: lack of information diversity leads to an increased polarization of viewpoints on various topics. While recommendation diversity can mitigate this, recommender systems need to balance their accuracy (i.e., recommending items the user will like) and diversity.
  • Behavior Influence and User Empowerment: people are easily influenced by small cues in both conscious and subconscious ways. Rather than letting technology exploit this influenceability, AI should be designed to empower the user, both by creating awareness and supporting the user to achieve their desired goals.