Explainable AI provides insights to users into the why for model predictions, offering potential for users to better understand and trust a model, and to recognize and correct AI predictions that are incorrect. Prior research on human and explainable AI interactions has focused on measures such as interpretability, trust, and usability of the explanation. There are mixed findings whether explainable AI can improve actual human decision-making and the ability to identify the problems with the underlying model. Using real datasets, we compare objective human decision accuracy without AI (control), with an AI prediction (no explanation), and AI prediction with explanation. We find providing any kind of AI prediction tends to improve user decision accuracy, but no conclusive evidence that explainable AI has a meaningful impact. Moreover, we observed the strongest predictor for human decision accuracy was AI accuracy and that users were somewhat able to detect when the AI was correct vs. incorrect, but this was not significantly affected by including an explanation. Our results indicate that, at least in some situations, the why information provided in explainable AI may not enhance user decision-making, and further research may be needed to understand how to integrate explainable AI into real systems.

Transparency has become a critical need in machine learning (ML) applications. Designing transparent ML models helps increase trust, ensure accountability, and scrutinize fairness. Some organizations may opt-out of transparency to protect individuals’ privacy. Therefore, there is a great demand for transparency models that consider both privacy and security risks. Such transparency models can motivate organizations to improve their credibility by making the ML-based decision-making process comprehensible to end-users. Differential privacy (DP) provides an important technique to disclose information while protecting individual privacy. However, it has been shown that DP alone cannot prevent certain types of privacy attacks against disclosed ML models. DP with low$\epsilon$values can provide high privacy guarantees, but may result in significantly weaker ML models in terms of accuracy. On the other hand, setting$\epsilon$value too high may lead to successful privacy attacks. This raises the question whether we can disclose accurate transparent ML models while preserving privacy. In this article we introduce a novel technique that complements DP to ensure model transparency and accuracy while being robust against model inversion attacks. We show that combining the proposed technique with DP provide highly transparent and accurate ML models while preserving privacy against model inversion attacks.

Over the years social network data has been mined to predict individuals' traits such as intelligence and sexual orientation. While mining social network data can provide many beneficial services to the user such as personalized experiences, it can also harm the user when used in making critical decisions such as employment. In this work, we investigate the reliability of applying data mining techniques on social network data to predict various individual traits. In spite of the preliminary success of such data mining applications, in this paper, we demonstrate the vulnerabilities of existing state of the art social network data mining techniques when they are facing malicious attacks. Our results indicate that making critical decisions, such as employment or credit approval, based solely on social network data mining results is still premature at this stage. Specifically, we explore Facebook likes data for predicting the traits of a Facebook user, including their political views and sexual orientation. We perform several types of malicious attacks on the predictive models to measure and understand their potential vulnerabilities. We find that existing predictive models built on social network data can be easily manipulated and suggest some countermeasures to prevent some of the proposed attacks.