Reliance on anonymity in social media has increased its popularity on these platforms among all ages. The availability of public Wi-Fi networks has facilitated a vast variety of online content, including social media applications. Although anonymity and ease of access can be a convenient means of communication for their users, it is difficult to manage and protect its vulnerable users against sexual predators. Using an automated identification system that can attribute predators to their text would make the solution more attainable. In this survey, we provide a review of the methods of pedophile attribution used in social media platforms. We examine the effect of the size of the suspect set and the length of the text on the task of attribution. Moreover, we review the most-used datasets, features, classification techniques and performance measures for attributing sexual predators. We found that few studies have proposed tools to mitigate the risk of online sexual predators, but none of them can provide suspect attribution. Finally, we list several open research problems.

True, when we write academic articles nowadays, nobody expects you to provide the trail of references all the way down to Aristotle. But few people would say that taking someone's recent NeurIPS paper and republishing it would be ok. Yes, it is a continuum, but it's still real. What exactly is common knowledge and what deserves a reference at a given point in time varies by person, depending on their domain knowledge and principles. Still, everybody has a fairly clear idea of what their own boundaries are. Would you personally be comfortable with changing some variable names in a StackOverflow snippet and passing it as your own work? Would you tell your child it's ok to copy-paste essay passages from public domain sources - after all, it's not illegal? How about if you hear an apt metaphor in someone's keynote that you haven't heard anywhere else - would you say that it's "just English" and use it as your own? Whatever your answers are to these questions - you have these answers, which means that you have your own attribution norms.

In this piece, we start by covering the important topic of marketing attribution and how AI approaches improve upon existing techniques. Attribution is one of the key issues in marketing these days. If a customer is exposed to ads via multiple advertising channels and finally converts, how should we attribute this conversion? The answer to this question is crucial for optimal budget allocation during future advertising campaigns. One of the simplest approaches is to assign all credit to the last ad clicked before a conversion.

The Shapley value has become a popular method to attribute the prediction of a machine-learning model on an input to its base features. The Shapley value [1] is known to be the unique method that satisfies certain desirable properties, and this motivates its use. Unfortunately, despite this uniqueness result, there are a multiplicity of Shapley values used in explaining a model's prediction. This is because there are many ways to apply the Shapley value that differ in how they reference the model, the training data, and the explanation context. In this paper, we study an approach that applies the Shapley value to conditional expectations (CES) of sets of features (cf. [2]) that subsumes several prior approaches within a common framework. We provide the first algorithm for the general version of CES. We show that CES can result in counterintuitive attributions in theory and in practice (we study a diabetes prediction task); for instance, CES can assign non-zero attributions to features that are not referenced by the model. In contrast, we show that an approach called the Baseline Shapley (BS) does not exhibit counterintuitive attributions; we support this claim with a uniqueness (axiomatic) result. We show that BS is a special case of CES, and CES with an independent feature distribution coincides with a randomized version of BS. Thus, BS fits into the CES framework, but does not suffer from many of CES's deficiencies.