Critical and Ethical Studies of Digital and Social Media
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ItemMatch Made by Humans: A Critical Enquiry into Human-Machine Configurations in Data Labelling( 2023-01-03)We present a critical ethnographic study of data labelling conducted in a Bangalore-based AI start-up. Labelled datasets are primarily produced by human data workers. We explore how humans and machines are configured together in data labelling and what are the demands placed on human workers, including on their body and cognition, while being assigned in the service of machine intelligence. We also show how these human-machine configurations sustain and reproduce the seamless functioning of apparently “autonomous” AI as a normative vision. Though labelled datasets are an indispensable prerequisite to creating ML/AI-based systems, the human labour that produces these datasets cannot be acknowledged fully if the techno-entrepreneurial vision of “self-learning” machine intelligence is to be celebrated and sustained. In pursuit of a normative position of what AI should be, we are left with a denial of how AI is actually produced now.
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ItemEthics, Power and Agency in Transnational Qualitative Methods: Remote Collaboration in a Refugee Camp( 2023-01-03)This is a methodological paper discussing the ethical and power-based implications of conducting remote and participatory research in a refugee camp. The paper presents the methodological approach to a community-engaged project studying education and technology in a refugee camp in Malawi. Half of the research team lives in Canada and half in the Dzaleka Refugee Camp. This paper reflects on this study, with a focus on remote partnership, team building with community researchers, and the use of digital tools to coordinate and manage data collection and analysis. We also identify locations where ethics and power are both disrupted and affirmed in this participatory study.
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Item"We Care About the Internet; We Care About Everything" Understanding Social Media Content Moderators' Mental Models and Support Needs( 2023-01-03)Despite the growing prevalence of ML algorithms, NLP, algorithmically-driven content recommender systems and other computational mechanisms on social media platforms, some of their core and mission-critical gatekeeping functions are nonetheless deeply reliant on the persistence of humans-in-the-loop to both validate computational models in use, and to intervene when those models fail. Perhaps nowhere is this human interaction with/on behalf of computation more key than in social media content moderation, where human capacities for discretion, discernment and the holding of complex mental models of decision-trees and changing policy are called upon hundreds, if not thousands, of times per day. This paper presents the results of a qualitative, interview-based study of an in-house content moderation team (Trust & Safety, or T&S) at a mid-size, erstwhile niche social platform we call FanClique. Findings indicate that while the FanClique T&S team is treated well in terms of support from managers, respect and support from the wider company, and mental health services provided (particularly in comparison to other social media companies), the work of content moderation remains an extremely taxing form of labor that is not adequately compensated or supported.
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