The real-world deployment of Deep Neural Networks (DNNs) in safety-critical applications such as autonomous vehicles needs to address a variety of DNNs' vulnerabilities, one of which being detecting and rejecting out-of-distribution outliers that might result in unpredictable fatal errors. We propose a new technique relying on self-supervision for generalizable out-of-distribution (OOD) feature learning and rejecting those samples at the inference time. Our technique does not need to pre-know the distribution of targeted OOD samples and incur no extra overheads compared to other methods. We perform multiple image classification experiments and observe our technique to perform favorably against state-of-the-art OOD detection methods. Interestingly, we witness that our method also reduces in-distribution classification risk via rejecting samples near the boundaries of the training set distribution.

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In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fakeness of news items, we jointly consider both attributes (e.g., speaker) and statements. Specifically, MIMIC, ATTN and PERT frameworks are designed, where MIMIC is built for attribute analysis, ATTN is for statement semantic analysis and PERT is for statement linguistic analysis. Beyond the explanations extracted from the designed frameworks, relevant supporting examples as well as visualization are further provided to facilitate the interpretation. Our implemented system is demonstrated on a real-world dataset crawled from PolitiFact 1 , where thousands of verified political news have been collected. CCS CONCEPTS• Human-centered computing → Natural language interfaces; Graphical user interfaces; Web-based interaction.

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Machine learning and artificial intelligence algorithms can assist human decision making and analysis tasks. While such technology shows promise, willingness to use and rely on intelligent systems may depend on whether people can trust and understand them. To address this issue, researchers have explored the use of explainable interfaces that attempt to help explain why or how a system produced the output for a given input. However, the effects of meaningful and meaningless explanations (determined by their alignment with human logic) are not properly understood, especially with users who are non-experts in data science. Additionally, we wanted to explore how explanation inclusion and level of meaningfulness would affect the user’s perception of accuracy. We designed a controlled experiment using an image classification scenario with local explanations to evaluate and better understand these issues. Our results show that whether explanations are human-meaningful can significantly affect perception of a system’s accuracy independent of the actual accuracy observed from system usage. Participants significantly underestimated the system’s accuracy when it provided weak, less human-meaningful explanations. Therefore, for intelligent systems with explainable interfaces, this research demonstrates that users are less likely to accurately judge the accuracy of algorithms that do not operate based on human-understandable rationale.

In the recent years, many heuristic optimization algorithms have been developed. A majority of these heuristic algorithms have been derived from the behavior of biological or physical systems in nature. In this paper, we propose a new optimization algorithm based on competitive behavior of animal groups. In the proposed algorithm, the whole population is divided into a number of groups. In each group, the best searching agent spreads its children in its owned territory. Any group which is not able to find rich resources will be eliminated form competition. The competition gradually results in an increase in population of wealthy group which gives a fast convergence to proposed optimization algorithm. In the following, after a detailed explanation of the algorithm and pseudo code, we compare it to other existing algorithms, including genetics and particle swarm optimizations. Applying the proposed algorithm on various benchmark cost functions, shows faster and superior results compared to other optimization algorithms.

Combating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale propagation of false and fabricated information with users being exposed to algorithmically selected false content. Our research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms to study the effects of algorithmic transparency on end-users. We present evaluation results and analysis from multiple controlled crowdsourced studies. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining. The study results indicate that explanations helped participants to build appropriate mental models of the intelligent assistants in different conditions and adjust their trust according to their perceptions of model limitations.

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