Ethical Considerations of Including Personal Demographic Information in Open Knowledge Platforms
Bibliographic record
Abstract
In recent years, galleries, libraries, archives, and museums (GLAMs) have sought to leverage open knowledge platforms such as Wikidata to highlight or provide more visibility for traditionally marginalized groups and their work, collections, or contributions. Efforts like Art + Feminism, local edit-a-thons, and, more recently, GLAM institution-led projects have promoted open knowledge initiatives to a broader audience of participants. One such open knowledge project, the Program for Cooperative Cataloging (PCC) Wikidata Pilot, has brought together over seventy GLAM organizations to contribute linked open data for individuals associated with their institutions, collections, or archives. However, these projects have brought up ethical concerns around including potentially sensitive personal demographic information, such as gender identity, sexual orientation, race, and ethnicity, in entries in an open knowledge base about living persons. GLAM institutions are thus in a position of balancing open access with ethical cataloging, which should include adhering to the personal preferences of the individuals whose data is being shared. People working in libraries and archives have been increasingly focusing their energies on issues of diversity, equity, and inclusion in their descriptive practices, including remediating legacy data and addressing biased language. Moving this work into a more public sphere and scaling up in volume creates potential risks to the individuals being described. While adding demographic information on living people to open knowledge bases has the potential to enhance, highlight, and celebrate diversity, it could also potentially be used to the detriment of the subjects through surveillance and targeting activities. In this article we seek to investigate the changing role of metadata and open knowledge in addressing, or not addressing, issues of under- and misrepresentation, especially as they pertain to gender identity as described in the sex or gender property in Wikidata. We report findings from a survey investigating how organizations participating in open knowledge projects are addressing ethical concerns around including personal demographic information as part of their projects, including what, if any, policies they have implemented and what implications these activities may have for the living people being described.
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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 | 0.352 | 0.438 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".