Design Justice
By Sasha Costanza-Chock. 2018
Design is key to our collective liberation, but most design processes today reproduce inequalities structured by what Black feminist scholars call the matrix of domination. Intersecting inequalities are manifest at all levels of the design process. This paper builds upon the Design Justice Principles, developed by an emerging network of designers and community organizers, to propose a working definition of design justice: Design justice is a field of theory and practice that is concerned with how the design of objects and systems influences the distribution of risks, harms, and benefits among various groups of people. Design justice focuses on the ways that design reproduces, is reproduced by, and/or challenges the matrix of domination (white supremacy, heteropatriarchy, capitalism, and settler colonialism). Design justice is also a growing social movement that aims to ensure a more equitable distribution of design’s benefits and burdens; fair and meaningful participation in design decisions; and recognition of community based design traditions, knowledge, and practices.
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Citations in the corpus
- Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “…how, for whom, and when to implement research outcomes should involve those that may be affected or whose data is being used (Costanza-Chock, 2018; Design Justice, 2018; Green, 2018)—following the “ nothing about us without us ” principle stating that no policy should be enacted without the…”
- Human-Computer Insurrection: Notes on an Anarchist HCI (2019, unknown): “ might remake HCI to privilege impacted communities. This remaking would contribute to more equitable inter- and intra-community distribution ofbothparticipation and the benefits and burdens of design [35]. A related accountability practice could be to alter the peer review processtoincludecommunityr”
- Critical Perspectives on Governance Mechanisms for AI/ML Systems (2020, unknown): “…this and much related scholarship is the necessity of foregrounding and centering the expertise of communities affected by AI/ML systems in their design and use (Costanza-Chock, 2018; Forlano & Mathew, 2013; Madaio, Stark, Wortman Vaughan, & Wallach, 2020; Sloan, Moss, Awomolo, & Forlano, 2020).”