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Prior research has highlighted opportunities for technology to better support the tabletop game experience in offline and online settings, but little work has focused on the social aspect of tabletop gaming. We investigated the social and collaborative aspects of tabletop gaming in the unique context of “social distancing” during the 2020 COVID-19 pandemic to shed light on the experience of remote tabletop gaming. With a multi-method qualitative approach (including digital ethnography and in-depth interviews), we empirically studied how people appropriate existing technologies and adapt their offline practices to play tabletop games remotely. We identify three themes that describe people’s game and social experience during remote play: creating a shared tabletop environment (shared space), enabling a collective understanding (shared information and awareness), and facilitating a communal temporal experience (shared time). We reflect on challenges and design opportunities for a better experience in the age of remote collaboration. 

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.