A Solution for Explainable AI and Visual Knowledge Discovery
Bibliographic record
Abstract
In IV 2024, Leung et al. presented an explainable artificial intelligence (XAI) solution for the practical application of employee turnover. The solution combines cutting-edge techniques and enhances them to generate practical and comprehensible explanations for end-users. It provides users with the magnitude and sign of the bar in the waterfall plot. The color of the bar indicates the sign (i.e., serves a binary value to indicate whether the attribute is deviated numerically above or below the mean). However, the bar does not fully address the drawbacks of the waterfall plot. For example, it does not reveal the magnitude of deviation. To address these issues, we present an enhanced solution for XAI and visual knowledge discovery. To assess the effectiveness of our XAI solution, we conduct a case study using real-life employee churn data, specifically focusing on driver churn, obtained from a national trucking company. The results demonstrate the practicality and usefulness of our XAI solution in applications such as analyzing employee churn. By offering an XAI solution that integrates advanced techniques and prioritizes explainability, we aim to provide businesses with actionable insights into employee churn, enabling them to make informed decisions and mitigate turnover effectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".