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Policies significantly shape computation’s societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at the “border” of HCI and policy. This paper asks: What if HCI considers policy integral to its intellectual concerns, placing system-people-policy interaction not at the border but nearer the center of HCI research, practice, and education? What if HCI fosters a mosaic of methods and knowledge contributions that blend system, human, and policy expertise in various ways, just like HCI has done with blending system and human expertise? We present this re-imagined HCI-policy relationship as a provocation and highlight its usefulness: It spotlights previously overlooked system-people-policy interaction work in HCI. It unveils new opportunities for HCI’s futuring, empirical, and design projects. It allows HCI to coordinate its diverse policy engagements, enhancing its collective impact on policy outcomes. 

Optimization is offered as an objective approach to resolving com- plex, real-world decisions involving uncertainty and conflicting interests. It drives business strategies as well as public policies and, increasingly, lies at the heart of sophisticated machine learning systems. A paradigm used to approach potentially high-stakes de- cisions, optimization relies on abstracting the real world to a set of decision(s), objective(s) and constraint(s). Drawing from the mod- eling process and a range of actual cases, this paper describes the normative choices and assumptions that are necessarily part of us- ing optimization. It then identifies six emergent problems that may be neglected: 1) Misspecified values can yield optimizations that omit certain imperatives altogether or incorporate them incorrectly as a constraint or as part of the objective, 2) Problematic decision boundaries can lead to faulty modularity assumptions and feedback loops, 3) Failing to account for multiple agents’ divergent goals and decisions can lead to policies that serve only certain narrow inter- ests, 4) Mislabeling and mismeasurement can introduce bias and imprecision, 5) Faulty use of relaxation and approximation methods, unaccompanied by formal characterizations and guarantees, can severely impede applicability, and 6) Treating optimization as a justification for action, without specifying the necessary contex- tual information, can lead to ethically dubious or faulty decisions. Suggestions are given to further understand and curb the harms that can arise when optimization is used wrongfully. 

Accounting for technologies’ unintended consequences—whether they are misinformation on social media or issues of sustainability and social justice—increasingly requires HCI to consider technology design at a societal-level scale. At this scale, public and corporate policies play a critical role in shaping technologies and user behaviors. However, the research and practices around tech and policy design have largely been held separate. How can technology design and policies better inform and coordinate with each other in generating safe new technologies? What new solutions might emerge when HCI practitioners design technology and its policies simultaneously to account for its societal impacts? This workshop addresses these questions. It will 1) identify disciplines and areas of expertise needed for a tighter, more proactive technology-and-policy-design integration, 2) launch a community of researchers, educators, and designers interested in this integration, 3) identify and publish an HCI research and education agenda towards designing technologies and technology policies simultaneously.