arXiv:2512.10659v3 Announce Type: replace Abstract: Outlier detection identifies data points that significantly deviate from the majority of the data distribution. Explaining outliers is crucial for understanding the underlying factors that contribute to their detection, validating their significance, and identifying potential biases or errors. Effective explanations provide actionable insights, facilitating preventive measures to avoid similar outliers in the future. Counterfactual explanations clarify why specific data points are classified as outliers by identifying minimal changes required
The increasing complexity and opacity of AI models necessitate more advanced methods to interpret their decisions, especially in critical applications like outlier detection.
Sophisticated outlier explanation is crucial for validating AI systems, identifying biases, and ensuring trust in automated decision-making processes across various sectors.
This research contributes to the evolving field of explainable AI (XAI), enhancing the ability to understand why specific data points are flagged as anomalous, making AI more transparent and accountable.
- · AI developers and researchers
- · Industries relying on anomaly detection (e.g., finance, cybersecurity)
- · Users of AI systems
- · Opaque black-box AI systems
- · Manual data anomaly investigation processes
Improved understanding and debugging of AI models for outlier detection.
Increased adoption of AI in sensitive applications due to enhanced interpretability and trust.
New regulatory requirements for explainability in AI, further accelerating XAI research and implementation.
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Read at arXiv cs.LG