  Computational social scientists often harness the Web as a "societal
observatory" where data about human social behavior is collected. This data
enables novel investigations of psychological, anthropological and sociological
research questions. However, in the absence of demographic information, such as
gender, many relevant research questions cannot be addressed. To tackle this
problem, researchers often rely on automated methods to infer gender from name
information provided on the web. However, little is known about the accuracy of
existing gender-detection methods and how biased they are against certain
sub-populations. In this paper, we address this question by systematically
comparing several gender detection methods on a random sample of scientists for
whom we know their full name, their gender and the country of their workplace.
We further suggest a novel method that employs web-based image retrieval and
gender recognition in facial images in order to augment name-based approaches.
Our findings show that the performance of name-based gender detection
approaches can be biased towards countries of origin and such biases can be
reduced by combining name-based an image-based gender detection methods.
