On Two XAI Cultures: A Case Study of Non-technical Explanations in Deployed AI System

Explainable AI (XAI) research has been booming, but the question "$\textbf{To whom}$ are we making AI explainable?" is yet to gain sufficient attention. Not much of XAI is comprehensible to non-AI experts, who nonetheless, are the primary audience and major stakeholders of deployed AI systems in practice. The gap is glaring: what is considered "explained" to AI-experts versus non-experts are very different in practical scenarios. Hence, this gap produced two distinct cultures of expectations, goals, and forms of XAI in real-life AI deployments. We advocate that it is critical to develop XAI methods for non-technical audiences. We then present a real-life case study, where AI experts provided non-technical explanations of AI decisions to non-technical stakeholders, and completed a successful deployment in a highly regulated industry. We then synthesize lessons learned from the case, and share a list of suggestions for AI experts to consider when explaining AI decisions to non-technical stakeholders.

Usable Security for ML Systems in Mental Health: A Framework

While the applications and demands of Machine learning (ML) systems in mental health are growing, there is little discussion nor consensus regarding a uniquely challenging aspect: building security methods and requirements into these ML systems, and keep the ML system usable for end-users. This question of usable security is very important, because the lack of consideration in either security or usability would hinder large-scale user adoption and active usage of ML systems in mental health applications. In this short paper, we introduce a framework of four pillars, and a set of desired properties which can be used to systematically guide and evaluate security-related designs, implementations, and deployments of ML systems for mental health. We aim to weave together threads from different domains, incorporate existing views, and propose new principles and requirements, in an effort to lay out a clear framework where criteria and expectations are established, and are used to make security mechanisms usable for end-users of those ML systems in mental health. Together with this framework, we present several concrete scenarios where different usable security cases and profiles in ML-systems in mental health applications are examined and evaluated.