When Robots Err: Psychological Pitfalls in Human-Robot Interactions

Nowadays, robots are much more than simple machine tools that assist people in their daily lives. With technological advances, they have taken on a central role in many areas, assisting users in their decisions and actions as social partners. While this can be advantageous when robots function flawlessly, enhancing efficiency and performance for users, it also poses risks when robots make mistakes and these are accepted without hesitance. In this context, human error occurs as a result of overreliance on the robot. This dissertation addresses the problem of overreliance on faulty robots from a psychological perspective. It aims to uncover psychological mechanisms, based on cognitive biases, and their influencing factors that contribute to this problem and are specific to human-robot interaction (HRI). An overarching conceptual framework was first developed through a literature review in robotics psychology (conceptual research) to identify and structure general psychological processes within HRI. This showed that cognitive biases in particular are a bottleneck for erroneous robot use. Consequently, two empirical research projects, each consisting of two empirical studies, were conducted to investigate the phenomenon of automation bias (AB) (empirical research project 1) and the belief and spread of fake news (empirical research project 2) in the context of HRI. This contributes to gaining insights into the underlying psychological mechanisms of erroneous reliance on faulty robots with individual and collective implications. The first empirical research project uses a laboratory experiment in a company (study 1.1) and an online experiment (study 1.2) and is based on the theoretical foundations of the “Automation Authority and Superiority Theory” and the “Diffusion of Responsibility Theory”. Study 1.1 shows that users are subject to AB in their perception and behavior when dealing with a faulty robot. Users engage in an implicit ability comparison with the robot, in which they perform worse than the robot and, as a result, defer to the robot’s supposed superiority. On this basis, they comply with the faulty robot. AB is also influenced by an interaction between perceived robot competence and task complexity. Study 1.2 shows that when dealing with a faulty robot, users are so strongly influenced by AB in their perception and behavior that their decision-making quality is impaired. Emotional tasks strengthen this effect, while high reliability of the robot weakens it. It is also shown across both studies that AB indirectly reduces satisfaction with the robot, indicating a negative consequence of AB. The second empirical research project consists of an online experiment (Study 2.1) and a laboratory experiment (Study 2.2) and is based on the “Signal Detection Theory” to measure the belief in fake news using the two indicators “sensitivity” and “response bias”. It shows that people are more susceptible to fake news in both online and face-to-face situations when it is delivered by robots. Compared to fake news transmitted by humans, they have a high tendency to carelessly accept news from robots and a reduced ability to identify fake news. Both studies show that this discriminatory ability is reinforced by a triad of cognitive biases due to robot-, user-, and interaction-specific factors. Finally, the study emphasizes that a reduced ability to discriminate increases the spread of fake news, indicating the amplyfying role of the robot as a medium for fake news dissemination. In sum, this dissertation contributes to the research field of robotic psychology by expanding the understanding of the mutual influence between humans and robots from a psychological perspective. Specifically, it derives a coherent theoretical framework of psychological processes that takes into account the specific characteristics of HRI. In addition, this dissertation advances the understanding of the psychological mechanisms of biased HRI – characterized by the erroneous reliance on a faulty robot due to cognitive biases – by extending the phenomena of AB as well as belief and spread of fake news to the domain of HRI and identifying robot-, user-, and context-related influencing factors. This allows for a more holistic view of biased HRI. Finally, practical implications are derived to guide the handling of biased HRI more towards trustful and responsible HRI.