Algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial research in recent years to build fair decision-making algorithms, there has been less research seeking to understand the factors that affect people's perceptions of fairness in these systems, which we argue is also important for their broader acceptance. In this research, we conduct an online experiment to better understand perceptions of fairness, focusing on three sets of factors: algorithm outcomes, algorithm development and deployment procedures, and individual differences. We find that people rate the algorithm as more fair when the algorithm predicts in their favor, even surpassing the negative effects of describing algorithms that are very biased against particular demographic groups. We find that this effect is moderated by several variables, including participants' education level, gender, and several aspects of the development procedure. Our findings suggest that systems that evaluate algorithmic fairness through users' feedback must consider the possibility of outcome favorability bias.
Safety in human-robot interaction can be divided into physical safety and perceived safety, where the latter is still under-addressed in the literature. Investigating perceived safety in human-robot interaction requires a multidisciplinary perspective. Indeed, perceived safety is often considered as being associated with several common factors studied in other disciplines, i.e., comfort, predictability, sense of control, and trust. In this paper, we investigated the relationship between these factors and perceived safety in human-robot interaction using subjective and objective measures. We co…
Previous research has shown that the fairness and the legitimacy of a moral decision-maker are important for people's acceptance of and compliance with the decision-maker. As technology rapidly advances, there have been increasing hopes and concerns about building artificially intelligent entities that are designed to intervene against norm violations. However, it is unclear how people would perceive artificial moral regulators that impose punishment on human wrongdoers. Grounded in theories of psychology and law, we predict that the perceived fairness of punishment imposed by a robot would in…
While visual comparison of directed acyclic graphs (DAGs) is commonly encountered in various disciplines (e.g., finance, biology), knowledge about humans' perception of graph similarity is currently quite limited. By graph similarity perception we mean how humans perceive commonalities and differences in graphs and herewith come to a similarity judgment. As a step toward filling this gap the study reported in this paper strives to identify factors which influence the similarity perception of DAGs. In particular, we conducted a card-sorting study employing a qualitative and quantitative analysi…
While research continues to investigate and improve the accuracy, fairness, and normative appropriateness of content moderation processes on large social media platforms, even the best process cannot be effective if users reject its authority as illegitimate. We present a survey experiment comparing the perceived institutional legitimacy of four popular content moderation processes. We conducted a within-subjects experiment in which we showed US Facebook users moderation decisions and randomized the description of whether those decisions were made by paid contractors, algorithms, expert panels…
Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. In this work, we conduct a human subject study to assess people's perceptions of informational fairness (i.e., whether people think they are given adequate information on and explanation of the process and its outcomes) and trustworthiness of an underlying ADS when provided with varying types of information about the system. More specifically, we instantia…
Algorithmic Accuracy as a Motivational Driver in Robot-Mediated Learning: A Comparative Study of Cross-Correlation and CNN-Based Sound Detection in an Interactive Quiz Game
Humboldt University of Berlin（柏林洪堡大学）；Berlin University of Applied Science（柏林应用科学大学）
