The HEIC application framework for implementing XAI-based socio-technical systems

The development of data-driven Artificial Intelligence systems has seen successful application in diverse domains related to social platforms; however, many of these systems cannot explain the rationale behind their decisions. This is a major drawback, especially in critical domains such as those related to cybersecurity, of which malicious behavior on social platforms is a clear example. In light of this problem, in this paper we make several contributions: (i) a proposal of desiderata for the explanation of outputs generated by AI-based cybersecurity systems; (ii) a review of approaches in the literature on Explainable AI (XAI) under the lens of both our desiderata and further dimensions that are typically used for examining XAI approaches; (iii) the Hybrid Explainable and Interpretable Cybersecurity (HEIC) application framework that can serve as a roadmap for guiding R&D efforts towards XAI-based socio-technical systems; (iv) an example instantiation of the proposed framework in a news recommendation setting, where a portion of news articles are assumed to be fake news; and (v) exploration of various types of explanations that can help different kinds of users to identify real vs. fake news in social platform settings.