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Daddy Issues: Love and Hate in the Time of Patriarchy, written by Katherine Angel, is a collection of essays that explore various aspects of patriarchal systems and their impact on [...]

This article investigates digital and non-digital traces reused beyond the context of creation. A central idea of this article is that no (reused) dataset is perfect. Therefore, data quality assessment becomes essential to determine if a given dataset is "good enough" to be used to fulfill the users' goals. Biases, a possible source of discrimination, have become a relevant data challenge. Consequently, it is appropriate to analyze whether quality assessment indicators provide information on potential biases in the dataset. We use examples representing two opposing sides regarding data access to reflect on the relationship between quality and bias. First, the European Union open data portal fosters the democratization of data and expects users to manipulate the databases directly to perform their analyses. Second, online behavioral advertising systems offer individualized promotional services but do not share the datasets supporting their design. Quality assessment is socially constructed, as there is not a universal definition but a set of quality dimensions, which might change for each professional context. From the users' perspective, trust/credibility stands out as a relevant quality dimension in the two analyzed cases. Results show that quality indicators (whatever they are) provide limited information on potential biases. We suggest that data literacy is most needed among both open data users and clients of behavioral advertising systems. Notably, users must (be able to) understand the limitations of datasets for an optimal and bias-free interpretation of results and decision-making. [ABSTRACT FROM AUTHOR]

This article attends to age stereotypes and ageism in contemporary tech. In academia, little attention has been devoted to this topic. Therefore, we intend to initiate a discussion around ageism in tech by studying perceptions of age in the tech industry. Our study is based on interviews with 18 tech workers around the world of varying age. According to our interviewees, tech workers over 35 are considered old in the tech industry. Older tech workers are expected to become managers, thought to become less interested in new technology, and expected to have more challenges when learning new software. We also look at how tech workers of different age groups experience entrepreneurial values of the company as a playground, staying hungry, and changing the future with technology, and how these values influence their professional careers. We conclude that ageism is reinforced in contemporary tech through several stereotypes related to age.

Digital systems can track every activity. Their logs are the fundamental raw material of intelligent systems in big data approaches. However, big data approaches mainly use predictions and correlations that often fail in the prediction of minorities or invisibilize collectives, causing discriminatory decisions. While this discrimination has been documented regarding, sex, race and sexual orientation, age has received less attention. A critical review of the academic literature confirms that structural ageism also shapes big data approaches. The article identifies some instances in which ageism is in operation either implicitly or explicitly. Concretely, biased samples and biased tools tend to exclude the habits, interests and values of older people from algorithms and studies, which contributes to reinforcing structural ageism.