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Add to Calendar 3/10/21 12:00 3/10/21 13:45 UTC Paper Session 21 Check out this session on the FAccT Hub.
Track One

Paper Session 21

Session Chair:
Maria Rodriguez
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Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies

Nicholas Vincent, Hanlin Li, Nicole Tilly, Stevie Chancellor, Brent Hecht
View Paper


Many powerful computing technologies rely on implicit and explicit data contributions from the public. This dependency suggests a potential source of leverage for the public in its relationship with technology companies: by reducing, stopping, redirecting, or otherwise manipulating data contributions, the public can reduce the effectiveness of many lucrative technologies. In this paper, we synthesize emerging research that seeks to better understand and help people action this \textit{data leverage}. Drawing on prior work in areas including machine learning, human-computer interaction, and fairness and accountability in computing, we present a framework for understanding data leverage that highlights new opportunities to change technology company behavior related to privacy, economic inequality, content moderation and other areas of societal concern. Our framework also points towards ways that policymakers can bolster data leverage as a means of changing the balance of power between the public and tech companies.

Documenting Computer Vision Datasets: An Invitation to Reflexive Data Practices

Milagros Miceli, Tianling Yang, Laurens Naudts, Martin Schuessler, Diana Serbanescu, Alex Hanna
View Paper


In industrial computer vision, discretionary decisions surrounding the production of image training data remain widely undocumented. Recent research taking issue with such opacity has proposed standardized processes for dataset documentation. In this paper, we expand this space of inquiry through fieldwork at two data processing companies and thirty interviews with data workers and computer vision practitioners. We identify four key issues that hinder the documentation of image datasets and the effective retrieval of production contexts. Finally, we propose reflexivity, understood as a collective consideration of social and intellectual factors that lead to praxis, as a necessary precondition for documentation. Reflexive documentation can help to expose the contexts, relations, routines, and power structures that shape data.

Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems

Joshua A. Kroll
View Paper


Accountability is widely understood as a goal for well governed computer systems, and is a sought-after value in many governance contexts. But how can it be achieved? Recent work on standards for governable artificial intelligence systems offers a related prin- ciple: traceability. Traceability requires establishing not only how a system worked but how it was created and for what purpose, in a way that explains why a system has particular dynamics or behaviors. It connects records of how the system was constructed and what the system did mechanically to the broader goals of gov- ernance, in a way that highlights human understanding of that mechanical operation and the decision processes underlying it. We examine the various ways in which the principle of traceability has been articulated in AI principles and other policy documents from around the world, distill from these a set of requirements on software systems driven by the principle, and systematize the technologies available to meet those requirements. From our map of requirements to supporting tools, techniques, and procedures, we identify gaps and needs separating what traceability requires from the toolbox available for practitioners. This map reframes existing discussions around accountability and transparency, using the principle of traceability to show how, when, and why trans- parency can be deployed to serve accountability goals and thereby improve the normative fidelity of systems and their development processes.

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