Whose Values Matter in Persuasive Writing Tools?
Bibliographic record
Abstract
We examine Microsoft’s Inclusivity Suggestions (MSIS) tool in promoting inclusive persuasive writing. In doing so, we tackle the question of how best to adapt to the plurality of humanness in technology design. Following the naturalistic use of the tool in an educational context, we conducted a qualitative investigation with nine diverse students to evaluate the tool’s capabilities and limitations. Our findings reveal that while MSIS effectively identifies explicit gender biases, it struggles with implicit biases, code-switching, and multilingual inclusivity. Participants perceived the tool as useful in raising awareness but highlighted notable differences between performative use and genuine engagement with inclusive language. Based on these insights, we argue the tool has strong biases towards an American-centered conception of diversity. Drawing on earlier work on value-sensitive design, we propose design recommendations, and more broadly we critique whether designing for universal values is entirely realistic. We call for a more international perspective on the value tensions regarding diversity embedded into technology. Content warning: racist and sexist data.
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 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".