Data-Responsible Technology Solutions for Fit in Online Fashion Retail
Bibliographic record
Abstract
This study discusses the datafication processes and their ethical implications in the conduct of virtual try-on (VTO) technologies for fit in the online fashion industry, current practices in the business of VTO, and the physiological and psychological complications that are critical to shoppers' evaluation of fit and sizing. VTO solutions currently work by applying Augmented Reality technology or Artificial Intelligence to visualize the shopper digitally "wearing" the product to help the shopper evaluate garment fit. However, shopper satisfaction is impacted by augmentation quality and user control of access to user data. Furthermore, hedonic and utilitarian aspects of shopping are mediated by the shopper's inherent body satisfaction and technology acceptance; hence, shoppers do not always relate positively to a 3D visualization (avatar) of themselves. At the same time, research shows shoppers are impacted positively by images of model bodies similar to theirs and that while using an AR VTO, their perception of augmentation quality is impacted by the feeling that they have control over the access and use of their own data. A conceptual framework was developed for critical analysis to review the product quality, technical quality, and data handling practices of 3 main case studies, and a summary of findings was produced using thematic, content, and psychometric analysis. The research presents its results through practice-led research to document the activities and processes involved in the creative practice of the development of a prototype for a data-responsible alternative solution for fit comparison and evaluation in virtual try-on, to provide users total freedom to provide or withhold their data without implications to the quality of service.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".