The social impacts of computer technology are often glorified in public discourse, but there is growing concern about its actual effects on society. In this article, we ask: how does “consent” as an analytical framework make visible the social dynamics and power relations in the capture, extraction, and labor of data science knowledge production? We hypothesize that a form of boundary violation in data science workplaces—gender harassment—may correlate with the ways humans’ lived experiences are extracted to produce Big Data. The concept of consent offers a useful way to draw comparisons between gender relations in data science and the means by which machines are trained to learn and reason. Inspired by how Big Tech leaders describe unsupervised machine learning, and the co-optation of “revolutionary” rhetoric they use to do so, we introduce a concept we call “techniques of invisibility.” Techniques of invisibility are the ways in which an extreme imbalance between exposure and opacity, demarcated along fault lines of power, are fabricated and maintained, closing down the possibility for bidirectional transparency in the production and applications of algorithms. Further, techniques of invisibility, which we group into two categories—epistemic injustice and the Brotherhood—include acts of subjection by powerful actors in data science designed to quell resistance to exploitative relations. These techniques may be useful in making further connections between epistemic violence, sexism, and surveillance, sussing out persistent boundary violations in data science to render the social in data science visible, and open to scrutiny and debate.
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The tragedy of lost ideas: examining epistemic injustice in pair programming
This paper explores an episode of epistemic injustice that develops between two students with help from two teachers. Our analysis seeks to demonstrate not only that epistemic injustice has occurred, but also, how, and why it matters. In particular, we explore the idea of credibility deficit as helping to account for how and why one student’s contributions were routinely sidelined or ignored, and how that repeated positioning led to the ultimate act of testimonial injustice and its outcome, a wrong in the form of a loss of opportunity to learn.
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- Award ID(s):
- 1742257
- NSF-PAR ID:
- 10311208
- Editor(s):
- de Vries, E.; Hod, Y.; null
- Date Published:
- Journal Name:
- Computersupported collaborative learning
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 1573-4552
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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If we are to support students to become epistemic agents in the ways envisioned in reforms, we must acknowledge that classrooms can be spaces of injustice, where instructional efforts can propagate inequitable systems of oppression. In this case study, we describe the epistemic efforts of one Black girl, Jessie, and the rights and privileges afforded or denied to her as she worked with a group of her peers to develop and negotiate a scientific claim. Through examination of video data, transcripts, and student work products, we characterized students’ efforts as about epistemic, rhetorical, and pseudo-argumentation, and how we explored how such efforts invited or constrained Jessie’s epistemic agency. Jessie’s pattern of persistence, which we understand to be her fight to have her rights as a scientific sensemaker acknowledged, surfaced issues of inequity in which Jessie’s ongoing efforts to engage in epistemic argumentation were rejected by her peers.more » « less
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Abstract Policy responses to the challenges associated with environmental change, including more frequent and severe climatic events, have interlinked environmental and social impacts. Less attention has been afforded to the latter, and specifically to the question of not just whether but how such responses create or entrench inequality. This paper examines policy responses to drought events in California, United States, and the Western Cape Province, South Africa, in terms of their effects on inequality, revealed in relationships to water access networks. We use concepts of water justice and hydraulic citizenship to evaluate how and why these policy responses reproduced water injustices in the two settings. We focus particularly on two mechanisms linking responses to widened inequalities:
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Abstract Attribution—the explanation of an observed change in terms of multiple causal factors—is the cornerstone of climate-change science. For anthropogenic climate change (ACC), the central causal factor is evidently ACC itself, and one of the primary tools used to reveal ACC is aggregation, or grouping together, of data, e.g. global mean surface temperature. Whilst this approach has served climate-change science well, the landscape is changing rapidly. First, there is an increasing focus on regional or local aspects of climate change, and on singular or unprecedented events, which require varying degrees of disaggregation. Relatedly, climate change is increasingly apparent in observations at the local scale, which is challenging the primacy of climate model simulations. Finally, the explosion of climate data is leading to more phenomena-laden methodologies such as machine learning. All this demands a re-think of how attribution is performed and causal explanations are constructed. Here we use Lloyd’s ‘Logic of Research Questions’ framework to show how the way in which the attribution question is framed can strongly constrain its possible and responsive answers. To address the Research Question ‘What was the effect of ACC on X?’ (RQ1), scientists generally consider the question ‘What were the causal factors leading to X, and was ACC among them?’. If the causal factors include only external forcing and internal variability (RQ2), then answering RQ2 also answers RQ1. However, this unconditional attribution is not always possible. In such cases, allowing the causal factors to include elements of the climate system itself (RQ3)—the conditional, storyline approach—is shown to allow for a wider range of possible and responsive answers than RQ2, including that of singular causation. This flexibility is important when uncertainties are high. As a result, the conditional RQ3 mitigates against the sort of epistemic injustice that can arise from the unconditional RQ2.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
