Introducing fairness in network visualization
Bibliographic record
Abstract
Motivated by the need for decision-making systems that avoid bias and discrimination, the concept of fairness recently gained traction in the broad field of artificial intelligence , stimulating new research also within the information visualization community. In this paper, we introduce a notion of fairness in network visualization, specifically for orthogonal and for straight-line drawings of graphs, two foundational paradigms in the field. We investigate the following research questions: (i) What is the price, in terms of global readability , of incorporating fairness constraints in graph drawings? (ii) How unfair is a graph drawing that does not optimize fairness as a primary objective ? We present both theoretical and empirical results. In particular, we design and implement two optimization algorithms for multi-objective functions, one based on an ILP model for orthogonal drawings, and one based on gradient descent for straight-line drawings. In a nutshell, we experimentally show that it is possible to significantly increase the fairness of a drawing by paying a relatively small amount in terms of reduced global readability. Also, we present a use case in which we qualitatively evaluate our approach on a practical scenario.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".