# Signatures to help interpretability of anomalies

arXiv: https://arxiv.org/abs/2506.16314 · HTML: https://mlsanity.com/paper/2506.16314/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Coniferest: a complete active anomaly detection framework](https://mlsanity.com/paper/2410.17142/) (2024)

## Abstract (arXiv)

Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the astronomer is left to independently analyze the data in order to understand why a given event is tagged as an anomaly. We introduce here idea of anomaly signature, whose aim is to help the interpretability of anomalies by highlighting which features contributed to the decision.