With the rise of big data, artificial intelligence (AI), and data mining techniques, group analysis has increasingly become a powerful tool in many applications, ranging from policy-making, direct marketing, education, to healthcare. For example, an important analysis strategy is group profiling, which extracts and describes the characteristics of groups of people [40]; it has been commonly used for customized recommendations to overcome sparse and missing personal data [25]. The same strategy is also used for mining social media, educational, and healthcare data to understand the shared characteristics of online communities or student/patient cohorts [15, 51, 100]. While it may help to support public and private services or product creations that are better tailored to different communities, group profiles resulted from mathematical inference are typically not valid for every individual regarded as a member in the group (this is known as non-distributive group profiles) [40]. The shared group characteristics extracted from data can have social ramifications such as stereotyping, stigmatization, or lead to pernicious consequences in decision making because individuals might be judged by group characteristics they do not posses [24, 56, 58].

An AI technique called group analysis, used to detect e-commerce fraud, holds great promise for catching fraud rings sooner rather than later. Insurance fraud scams seem to make the news at least every month, as organized criminals seek to exploit the way insurers reimburse clinics, pharmacies and other providers for their services. What's often shocking is how much money fraudsters can steal from insurers before they're caught. Recently, in a single month, two separate alleged fraud rings based in California were busted for scams that investigators say netted $20 million or more. Clearly, there's a need for fraud detection tools that can spot these frauds in their early stages.