Human-Computer Interaction and International Public Policymaking: A Framework for Understanding and Taking Future Actions
Bibliographic record
Abstract
This monograph lays out a discussion framework for understanding the role of human–computer interaction (HCI) in public policymaking. We take an international view, discussing potential areas for research and application, and their potential for impact. Little has been written about the intersection of HCI and public policy; existing reports typically focus on one specific policy issue or incident. To date, there has been no overarching view of the areas of existing impact and potential impact. We have begun that analysis and argue here that such a global view is needed. Our aims are to provide a solid foundation for discussion, cooperation and collaborative interaction, and to outline future programs of activity. The five sections of this report provide relevant background along with a preliminary version of what we expect to be an evolving framework. Sections 1 and 2 provides an introduction to HCI and public policy. Section 3 discusses how HCI already informs public policy, with representative examples. Section 4 discusses how public policy influences HCI and provides representative public policy areas relevant to HCI, where HCI could have even more impact in the future: (i) laws, regulations, and guidelines for HCI research, (ii) HCI research assessments, (iii) research funding, (iv) laws for interface design — accessibility and language, (v) data privacy laws and regulations, (vi) intellectual property, and (vii) laws and regulations in specific sectors. There is a striking difference between where the HCI community has had impact (Section 3) and the many areas of potential involvement (Section 4). Section 5 a framework for action by the HCI community in public policy internationally. This monograph summarizes the observations and recommendations from a daylong workshop at the CHI 2013 conference in Paris, France. The workshop invited the community's perspectives regarding the intersection of governmental policies, international and domestic standards, recent HCI research discoveries, and emergent considerations and challenges. It also incorporates contributions made after the workshop by workshop participants and by individuals who were unable to participate in the workshop but whose work and interests were highly related and relevant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.014 | 0.070 |
| Scholarly communication | 0.041 | 0.048 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".