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Full Description
Searching for a needle in a haystack is an important task in several contexts of data analysis and decision-making. Examples include identifying the insider threat within an organization, the prediction of failure in industrial production, or pinpointing the unique signature of a solo perpetrator, such as a school shooter or a lone wolf terrorist. It is a challenge different from that of identifying a rare event (e.g., a tsunami) or detecting anomalies because the "needle" is not easily distinguished from the haystack. This challenging context is imbued with particular difficulties, from the lack of sufficient data to train a machine learning model through the identification of the relevant features and up to the painful price of false alarms, which might cause us to question the relevance of machine learning solutions even if they perform well according to common performance criteria. In this book, Prof. Neuman approaches the problem of finding the needle by specifically focusing on the human factor, from solo perpetrators to insider threats. Providing for the first time a deep, critical, multidimensional, and methodological analysis of the challenge, the book offers data scientists and decision makers a deep scientific foundational approach combined with a pragmatic practical approach that may guide them in searching for a needle in a haystack.
Contents
Acknowledgments. Preface. 1 The Needle Challenge: From Shipping Vessels to the Insider Threat. 2 What Is a Needle in a Haystack?: A Lesson from Miss Lucy and Vladimir Putin. 3 How Are Rare Events Formed?: Modeling through the Galton Machine. 4 Crying Wolf: False Alarms and Their Price. 5 Why Is It Difficult to Find the Needle?: On Rare and Common Paths. 6 Why Do We Fail to Find the Needle?: The Binary Fallacy and the Bayesian Approach. 7 How to Reduce the Size of the Haystack: On Impostors, Cats, and False Positives. 8 Needles in the Wild: Some Lessons from Nature. 9 Lupus and the Needle: A Contextual-Dynamic Approach to the Needle Challenge. 10 How to Deal with Tiny Datasets: The Power of AI. 11 Concluding Discussion: Isolated Lights in the Abyss of Ignorance. Index.
