@inproceedings{BuettnerGoudarziPrilla2024, author = {B{\"u}ttner, Sebastian Thomas and Goudarzi, Maral and Prilla, Michael}, title = {Why does the robot only select men? How women and men perceive autonomous social robots that have a gender bias}, series = {MuC '24: Proceedings of Mensch und Computer 2024, Karlsruhe Germany September 1-4, 2024}, booktitle = {MuC '24: Proceedings of Mensch und Computer 2024, Karlsruhe Germany September 1-4, 2024}, publisher = {ACM Digital Library}, isbn = {979-8-4007-0998-2}, doi = {10.1145/3670653.3677492}, pages = {479 -- 484}, year = {2024}, abstract = {Abstract Future social robots will act autonomously in the world. Autonomous behavior is usually realized by using AI models built with real-world data, which often reflect existing inequalities and prejudices in society. Even if designers do not intend it, there are risks that robots will be developed that discriminate against certain users, e. g. based on gender. In this work, we investigate the implications of a gender-biased robot that disadvantages women, which unfortunately is a bias in AI that is often reported. Our experiment shows that both men and women perceive the gender-biased robot to be unfair. However, our work indicates that women are more aware that a gender bias causes this unfairness. We also show that gender bias results in the robot being perceived differently. While the gender bias resulted in lower likability and intelligence ratings by women, men seem to lose trust in the robot if it behaves unfairly.}, language = {en} } @article{AlhajiBuettnerKumaretal.2023, author = {Alhaji, Basel and B{\"u}ttner, Sebastian Thomas and Kumar, Shushanth Sanjay and Prilla, Michael}, title = {Trust dynamics in human interaction with an industrial robot}, series = {Behaviour \& Information Technology}, volume = {2025}, journal = {Behaviour \& Information Technology}, number = {Vol. 44, No. 2}, publisher = {Taylor \& Francis}, doi = {10.1080/0144929X.2024.2316284}, pages = {266 -- 288}, year = {2023}, abstract = {ABSTRACT Trust is important for collaboration. In hybrid teams of humans and robots, trust enables smooth collaboration and reduces risks. Just as collaboration between humans and robots differs from interpersonal collaboration, so does the nature of trust in human-robot interaction (HRI). Therefore, further investigations on trust formation and dissolution in HRI, factors affecting it, and means for keeping trust on an appropriate level are needed. However, our knowledge of interpersonal trust and trust in autonomous agents cannot be transferred directly to HRI. In this paper, we present a study with 32 participants on trust formation and dissolution as well as forecasting to influence trust in an industry robot. Results show differences in dynamics and factors of trust formation and dissolution. Additionally, we find that the effect of forecasting on trust depends on task success. These findings support the design of trustful human-robot interaction and corresponding robotic team members.}, language = {en} }