Card-Based Approach to Engage Exploring Ethics in AI for Data Visualization
Bibliographic record
Abstract
We present AI-VIS EthiCards, a card-based approach to explore ethics tailored for AI for visualization. The continuous integration of artificial intelligence and data visualization has brought about increased efficiency and benefits, yet inevitably raises ethical concerns. The emerging field of AI for visualization is marked by its inherent complexity, making it crucial for researchers, designers, and practitioners to cultivate ethical literacy and contemplate moral obligations within this intricate environment. These cards aim to aid users in learning, discussing, and reflecting on the ethical dilemmas that may arise from the integration of AI technology and visualization. The AI-VIS EthiCard set contains six themes: Goals, AI-VIS Tasks, Technologies, Ethical Principles, People-In-Focus, and Challenges, proposes various modes of use, including theoretical exploration, and design development simulations, with five activities. We aim to offer users an exploratory and open approach to discussions, providing multiple perspectives to guide ethical considerations when applying AI for visualization. The full set of cards is available at https://aivisethicards.github.io/.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 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".