Here, I ask what we can learn about how gender affects how people engage with robots. I review 46 empirical studies of social robots, published 2018 or earlier, which report on the gender of their participants or the perceived or intended gender of the robot, or both, and perform some analysis with respect to either participant or robot gender. From these studies, I find that robots are by default perceived as male, that robots absorb human gender stereotypes, and that men tend to engage with robots more than women. I highlight open questions about how such gender effects may be different in younger participants, and whether one should seek to match the gender of the robot to the gender of the participant to ensure positive interaction outcomes. I conclude by suggesting that future research should: include gender diverse participant pools, include non-binary participants, rely on self-identification for discerning gender rather than researcher perception, control for known covariates of gender, test for different study outcomes with respect to gender, and test whether the robot used was perceived as gendered by participants. I include an appendix with a narrative summary of gender-relevant findings from each of the 46 papers to aid in future literature reviews.
翻译:这里,我问我们可以了解什么性别如何影响人们如何与机器人打交道。我审查了关于社会机器人的46项经验研究,2018年或更早发表过,其中报告了参与者的性别或机器人的感知或意图性别,或两者兼有,并对参与者或机器人的性别进行了一些分析。从这些研究中,我发现机器人默认为男性,机器人吸收了人类的性别陈规定型观念,而男性往往与机器人接触多于女性。我强调一些开放的问题,即这种性别影响在年轻参与者中可能有何不同,以及是否应设法将机器人的性别与参与者的性别相匹配,以确保积极的互动结果。我最后建议,未来的研究应该包括性别多样性参与者群体,包括非二元参与者,依靠自我认同来辨别性别而不是研究者的看法,控制已知的性别变量,测试与性别有关的不同研究结果,以及测试参与者是否认为使用的机器人具有性别特征。我包括一个附录,其中附有46份文件中每一项与性别有关的调查结果的说明摘要,以帮助今后的文献审查。