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This article delves into the debates, both in print and digital media, surrounding copper mining and its associated concerns, particularly those related to hardrock mining. Guided by agenda-building, pragma-dialectics, and stakeholder theories, this research employs topic modeling to scrutinize the media strategies and arguments employed by key stakeholders involved in an environmental movement. The aim is to unveil implicit communication activities that contribute to the emergence of public debates, shedding light on the nuanced dynamics within stakeholder discourses. The analysis shows that by referring to authoritative arguments, the local press distributes the places of power while representing conflicting standpoints and arguments. With digital media, the opponents build a pro-environmentalist network to publicize the issues relative to the mining project and the importance of defending southwestern habitats and landscape. Mining proponents similarly rely on business communities to underscore the importance of hardrock mining for things like decarbonized energy and modern living. Across groups, stakeholder communication situates differing conceptions of the relationship between humans and their natural environment. Our findings reveal that, consistently across various groups, the discourse maintains a degree of stability as topics and arguments persist over time. On one side, discussions highlight the destructive impacts of mining, while the opposing perspective underscores the significance of sustainable and resilient mining practices. This analysis of stakeholder discourses in the media helps uncover the various ways primary actors maintain their positionality and power in this particular case.

AbstractOrganizations often employ data-driven models to inform decisions that can have a significant impact on people's lives (e.g., university admissions, hiring). In order to protect people's privacy and prevent discrimination, these decision-makers may choose to delete or avoid collecting social category data, like sex and race. In this article, we argue that such censoring can exacerbate discrimination by making biases more difficult to detect. We begin by detailing how computerized decisions can lead to biases in the absence of social category data and in some contexts, may even sustain biases that arise by random chance. We then show how proactively using social category data can help illuminate and combat discriminatory practices, using cases from education and employment that lead to strategies for detecting and preventing discrimination. We conclude that discrimination can occur in any sociotechnical system in which someone decides to use an algorithmic process to inform decision-making, and we offer a set of broader implications for researchers and policymakers.

AbstractOrganizations often employ data-driven models to inform decisions that can have a significant impact on people's lives (e.g., university admissions, hiring). In order to protect people's privacy and prevent discrimination, these decision-makers may choose to delete or avoid collecting social category data, like sex and race. In this article, we argue that such censoring can exacerbate discrimination by making biases more difficult to detect. We begin by detailing how computerized decisions can lead to biases in the absence of social category data and in some contexts, may even sustain biases that arise by random chance. We then show how proactively using social category data can help illuminate and combat discriminatory practices, using cases from education and employment that lead to strategies for detecting and preventing discrimination. We conclude that discrimination can occur in any sociotechnical system in which someone decides to use an algorithmic process to inform decision-making, and we offer a set of broader implications for researchers and policymakers.