Decoding Anomalies! Unraveling Operational Challenges in Human-in-the-Loop Anomaly Validation
Bibliographic record
Abstract
Artificial intelligence has been driving new industrial solutions for challenging problems in recent years, with many companies leveraging AI to enhance business processes and products. Automated anomaly detection emerges as one of the top priorities in AI adoption, sought after by numerous small to large-scale enterprises. Extending beyond domain-specific applications like software log analytics, where anomaly detection has perhaps garnered the most interest in software engineering, we find that very little research effort has been devoted to post-anomaly detection, such as validating anomalies. For example, validating anomalies requires human-in-the-loop interaction, though working with human experts is challenging due to uncertain requirements on how to elicit valuable feedback from them, posing formidable operationalizing challenges. In this study, we provide an experience report delving into a more holistic view of the complexities of adopting effective anomaly detection models from a requirement engineering perspective. We address challenges and provide solutions to mitigate challenges associated with operationalizing anomaly detection from diverse perspectives: inherent issues in dynamic datasets, diverse business contexts, and the dynamic interplay between human expertise and AI guidance in the decision-making process. We believe our experience report will provide insights for other companies looking to adopt anomaly detection in their own business settings.
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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 | 0.045 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".