RESUMO
Image recognition systems offer the promise to learn from images at scale without requiring expert knowledge. However, past research suggests that machine learning systems often produce biased output. In this article, we evaluate potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians. Our crowdsourced validation shows that commercial image recognition systems can produce labels that are correct and biased at the same time as they selectively report a subset of many possible true labels. We find that images of women received three times more annotations related to physical appearance. Moreover, women in images are recognized at substantially lower rates in comparison with men. We discuss how encoded biases such as these affect the visibility of women, reinforce harmful gender stereotypes, and limit the validity of the insights that can be gathered from such data.
RESUMO
There is some evidence that liberal politicians use more complex language than conservative politicians. This evidence, however, is based on a specific set of speeches of US members of Congress and UK members of Parliament. This raises the question whether the relationship between ideology and linguistic complexity is a more general phenomenon or specific to this small group of politicians. To address this question, this paper analyzes 381,609 speeches given by politicians from five parliaments, by twelve European prime ministers, as well as speeches from party congresses over time and across countries. Our results replicate and generalize earlier findings: speakers from culturally liberal parties use more complex language than speakers from culturally conservative parties. Economic left-right differences, on the other hand, are not systematically linked to linguistic complexity.
