With the widespread use of artificial intelligence and automated decision-making (ADM), concerns are increasing about automated decisions biased against certain social groups, such as women and racial minorities. The public's skepticism and the danger of algorithmic discrimination are widely acknowledged, yet the role of key factors constituting the context of discriminatory situations is underexplored. This study examined people’s perceptions of gender bias in ADM, focusing on three factors influencing the responses to discriminatory automated decisions: the target of discrimination (subject vs. other), the gender identity of the subject, and situational contexts that engender biases. Based on a randomised experiment (N = 602), we found stronger negative reactions to automated decisions that discriminate against the gender group of the subject than those discriminating against other gender groups, evidenced by lower perceived fairness and trust in ADM, and greater negative emotion and tendency to question the outcome. The negative reactions were more pronounced among participants in underserved gender groups than men. Also, participants were more sensitive to biases in economic and occupational contexts than in other situations. These findings suggest that perceptions of algorithmic biases should be understood in relation to the public's lived experience of inequality and injustice in society.

With the widespread use of artificial intelligence and automated decision-making (ADM), concerns are increasing about automated decisions biased against certain social groups, such as women and racial minorities. The public's skepticism and the danger of algorithmic discrimination are widely acknowledged, yet the role of key factors constituting the context of discriminatory situations is underexplored. This study examined people’s perceptions of gender bias in ADM, focusing on three factors influencing the responses to discriminatory automated decisions: the target of discrimination (subject vs. other), the gender identity of the subject, and situational contexts that engender biases. Based on a randomised experiment (N = 602), we found stronger negative reactions to automated decisions that discriminate against the gender group of the subject than those discriminating against other gender groups, evidenced by lower perceived fairness and trust in ADM, and greater negative emotion and tendency to question the outcome. The negative reactions were more pronounced among participants in underserved gender groups than men. Also, participants were more sensitive to biases in economic and occupational contexts than in other situations. These findings suggest that perceptions of algorithmic biases should be understood in relation to the public's lived experience of inequality and injustice in society.

Online sexism against female gamers is reportedly common and pervasive, causing serious problems. To help solve these problems, the study identified various predictors of online game sexism, which is hypothesised to predict actual in-game harassment. Different from previous studies, the study approaches the problems from the perspective of perpetrators rather than victims. We proposed a theoretical model that include three groups of predictors: offline sexist beliefs (masculine norms and hostile sexism), game-related factors (perceived territoriality, advancement, and competition), and environmental factors (peer harassment and play time). The model was tested against online survey data collected from a sample of 528 male gamers in South Korea with age range of 14–64 years (M = 34.70, SD = 12.81). The results showed that all the predictors, except competition and play time, were significantly associated with online game sexism, which mediated the relationships between the predictors and online sexual harassment. Perceived territoriality and peer harassment were found to have direct and positive effects on harassment. The findings are expected to contribute to developing more effective measures for preventing the hostility and aggression against female gamers by providing a new and more thorough diagnosis of the underlying causes of the problems.

Online sexism against female gamers is reportedly common and pervasive, causing serious problems. To help solve these problems, the study identified various predictors of online game sexism, which is hypothesised to predict actual in-game harassment. Different from previous studies, the study approaches the problems from the perspective of perpetrators rather than victims. We proposed a theoretical model that include three groups of predictors: offline sexist beliefs (masculine norms and hostile sexism), game-related factors (perceived territoriality, advancement, and competition), and environmental factors (peer harassment and play time). The model was tested against online survey data collected from a sample of 528 male gamers in South Korea with age range of 14–64 years (M = 34.70, SD = 12.81). The results showed that all the predictors, except competition and play time, were significantly associated with online game sexism, which mediated the relationships between the predictors and online sexual harassment. Perceived territoriality and peer harassment were found to have direct and positive effects on harassment. The findings are expected to contribute to developing more effective measures for preventing the hostility and aggression against female gamers by providing a new and more thorough diagnosis of the underlying causes of the problems.