“It looks like someone just threw random dots on a dot plot”: User Response to Sketchy Rendering Styles for Data-Driven Algorithmic Systems
Bibliographic record
Abstract
This paper examines how users respond to different rendering styles during early-stage interactions with data-driven algorithmic systems. Specifically, we examine how presenting an early version of a design in a “sketchy” style, designed to signal tentativeness and invite critique, may influence the user responses. We conducted a 2 × 2 think-aloud study using a prototype system developed to support data journalists in identifying outliers, manipulating both the underlying outlier detection model (Model A vs. Model B) and the visual rendering style of the interface (Sketchy vs. Polished). Results show that participants exposed to Sketchy renderings were more likely to suggest design changes, but only when interacting with one of the two models. Furthermore, the feedback elicited in the Sketchy condition was primarily aesthetic and surface-level rather than focused on the underlying data or algorithms. These findings suggest that sketchy rendering styles may encourage greater user engagement but not necessarily the kind of feedback that touches on the underlying aspects of the system, which designers of algorithmic systems may seek. We discuss the implications of these results for design practices, particularly prototyping fidelity, perceived system credibility, and the cultural assumptions embedded in design methods. This work contributes to a growing body of research examining how the presentation of design artifacts, such as through different rendering styles, may influence the kinds of feedback users provide during the development of algorithmic systems.
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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 | 0.011 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".