Search for: All records

As mobile devices become increasingly prevalent in society, the expected utility of such devices rises; arguably, the most impact comes from location-based services as they provide tremendous benefits to mobile users. These users also value privacy, i.e., keeping their locations and search queries private, but that is not easy to achieve. It has been previously proposed that user location privacy can be secured through the use of space filling curves due to their ability to preserve spatial proximity while hiding the actual physical locations. With a space filling curve, such as the Hilbert curve, an application that provides location-based services can allow the user to take advantage of those services without transmitting a physical location. Earlier research has uncovered vulnerabilities of such systems and proposed remedies. But those countermeasures were clearly aimed at reasonably large metropolitan areas. It was not clear if they were appropriate for small towns, which display sparsity of Points of Interest (POIs) and limited diversity in their categories. This paper studies the issue focusing on a small university town. 

While machine learning classifier models become more widely adopted, opaque “black-box” models remain mostly inscrutable for a variety of reasons. Since their applications increasingly involve decisions impacting the lives of humans, there is increasing demand that their predictions be understandable to humans. Of particular interest in eXplainable AI (XAI) is the interpretability of explanations, i.e., that a model’s prediction should be understandable in terms of the input features. One popular approach is LIME, which offers a model-agnostic framework for explaining any classifier. However, questions remain about the limitations and vulnerabilities of such post-hoc explainers. We have built a tool for generating synthetic tabular data sets which enables us to probe the explanation system opportunistically based on its architecture. In this paper, we report on our success in revealing a scenario where LIME’s explanation violates local faithfulness.