From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management

• URL: http://arxiv.org/abs/2304.03368v1
• Date: Thu, 6 Apr 2023 20:49:36 GMT
• Title: From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management
• Authors: Xueying Ding, Nikita Seleznev, Senthil Kumar, C. Bayan Bruss, Leman Akoglu
• Abstract summary: We introduce ALARM, an end-to-end framework that supports the anomaly mining cycle comprehensively. It offers anomaly explanations and an interactive GUI for human-in-the-loop processes. We demonstrate ALARM's efficacy through a series of case studies with fraud analysts from the financial industry.
• Score: 15.22568616519016
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, surveillance, to name a few. While the literature is abundant in effective detection algorithms due to this practical relevance, autonomous anomaly detection is rarely used in real-world scenarios. Especially in high-stakes applications, a human-in-the-loop is often involved in processes beyond detection such as verification and troubleshooting. In this work, we introduce ALARM (for Analyst-in-the-Loop Anomaly Reasoning and Management); an end-to-end framework that supports the anomaly mining cycle comprehensively, from detection to action. Besides unsupervised detection of emerging anomalies, it offers anomaly explanations and an interactive GUI for human-in-the-loop processes -- visual exploration, sense-making, and ultimately action-taking via designing new detection rules -- that help close ``the loop'' as the new rules complement rule-based supervised detection, typical of many deployed systems in practice. We demonstrate \method's efficacy through a series of case studies with fraud analysts from the financial industry.

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