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Science learning is thought to be best supported when students engage in sensemaking about phenomena in ways that mirror the work of scientists, work that requires that students are positioned as epistemic agents who share, discuss, and refine their thinking to make sense of science phenomena. Using a case study approach, we explore the experiences of one Black middle school girl, Jessie’s, epistemic efforts and the ways in which her group members’ responses to her efforts either supported or constrained her epistemic agency during small group work in two argumentation lessons. We view this work through the lenses of epistemic aspects of scientific argumentation, rhetorical argumentation, and pseudo argumentation. Our findings show that Jessie’s epistemic efforts were not often taken up by her peers in ways that support her epistemic agency, findings that have implications for student learning and engagement in terms of the epistemic work we ask students to engage in, and the instructional strategies that support this work.
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“Shadowbanning is not a thing”: black box gaslighting and the power to independently know and credibly critique algorithms
Efforts to govern algorithms have centerd the ‘black box problem,’ or the opacity of algorithms resulting from corporate secrecy and technical complexity. In this article, I conceptualize a related and equally fundamental challenge for governance efforts: black box gaslighting. Black box gaslighting captures how platforms may leverage perceptions of their epistemic authority on their algorithms to undermine users’ confidence in what they know about algorithms and destabilize credible criticism. I explicate the concept of black box gaslighting through a case study of the ‘shadowbanning’ dispute within the Instagram influencer community, drawing on interviews with influencers (n = 17) and online discourse materials (e.g., social media posts, blog posts, videos, etc.). I argue that black box gaslighting presents a formidable deterrent for those seeking accountability: an epistemic contest over the legitimacy of critiques in which platforms hold the upper hand. At the same time, I suggest we must be mindful of the partial nature of platforms’ claim to ‘the truth,’ as well as the value of user understandings of algorithms.
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- Award ID(s):
- 1946678
- PAR ID:
- 10312176
- Date Published:
- Journal Name:
- Information, Communication & Society
- ISSN:
- 1369-118X
- 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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Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse today. The opaque nature of the algorithms these platforms use to curate content raises societal questions. Prior studies have used black-box methods led by experts or collaborative audits driven by everyday users to show that these algorithms can lead to biased or discriminatory outcomes. However, existing auditing methods face fundamental limitations because they function independent of the platforms. Concerns of potential harmful outcomes have prompted proposal of legislation in both the U.S. and the E.U. to mandate a new form of auditing where vetted external researchers get privileged access to social media platforms. Unfortunately, to date there have been no concrete technical proposals to provide such auditing, because auditing at scale risks disclosure of users' private data and platforms' proprietary algorithms. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation. The first contribution of our work is to enumerate the challenges and the limitations of existing auditing methods to implement these policies at scale. Second, we suggest that limited, privileged access to relevance estimators is the key to enabling generalizable platform-supported auditing of social media platforms by external researchers. Third, we show platform-supported auditing need not risk user privacy nor disclosure of platforms' business interests by proposing an auditing framework that protects against these risks. For a particular fairness metric, we show that ensuring privacy imposes only a small constant factor increase (6.34x as an upper bound, and 4× for typical parameters) in the number of samples required for accurate auditing. Our technical contributions, combined with ongoing legal and policy efforts, can enable public oversight into how social media platforms affect individuals and society by moving past the privacy-vs-transparency hurdle.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/1369118X.2021.1994624
