Governance structures for new technologies are frequently top-down, reactive, and informally enforced, leaving marginalized communities with little power to address harms until after they occur. To address these limitations, we introduce Proactive Accountability, a conceptual framework theorizing that effective governance must be community-led, formally enforced, and continually maintained. We explore these principles through a speculative design study with Detroit’s food sovereignty community, in which participants identified community-owned cooperatives—described as “ancestral technologies”—as a model for redistributing power within a capitalist economy. Synthesizing these theoretical and empirical insights, we introduce the Designing for Proactive Accountability (D4PA) framework, providing implications for how designers can operationalize the goals of proactive accountability into HCI research and design projects. Finally, we contribute a future research agenda that positions cooperatives not merely as beneficiaries of design, but as sites of inquiry for understanding how to institutionalize justice-oriented democratic governance of sociotechnical systems.

In this paper, we examine computational approaches for measuring the "fairness" of image tagging systems, finding that they cluster into five distinct categories, each with its own analytic foundation. We also identify a range of normative concerns that are often collapsed under the terms "unfairness," "bias," or even "discrimination" when discussing problematic cases of image tagging. Specifically, we identify four types of representational harms that can be caused by image tagging systems, providing concrete examples of each. We then consider how different computational measurement approaches map to each of these types, demonstrating that there is not a one-to-one mapping. Our findings emphasize that no single measurement approach will be definitive and that it is not possible to infer from the use of a particular measurement approach which type of harm was intended to be measured. Lastly, equipped with this more granular understanding of the types of representational harms that can be caused by image tagging systems, we show that attempts to mitigate some of these types of harms may be in tension with one another.