Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem. Existing treatments of fairness rely on statistical correlations that can be fooled by statistical anomalies, such as Simpson's paradox. Proposals for causality-based definitions of fairness can correctly model some of these situations, but they require specification of the underlying causal models. In this paper, we formalize the situation as a database repair problem, proving sufficient conditions for fair classifiers in terms of admissible variables as opposed to a complete causal model. We show that these conditions correctly capture subtle fairness violations. We then use these conditions as the basis for database repair algorithms that provide provable fairness guarantees about classifiers trained on their training labels. We evaluate our algorithms on real data, demonstrating improvement over the state of the art on multiple fairness metrics proposed in the literature while retaining high utility.

We present GraviTIE (Global Representation and Visualization of Text and Image Embeddings, pronounced ”gravity”), an interactive visualization system for large-scale image datasets. GraviTIE operates on datasets consisting of images equipped with unstructured and semi-structured text, relying on multi-modal unsupervised learning methods to produce an interactive similarity map. Users interact with the similarity map through pan and zoom operations, as well as keyword-oriented queries. GraviTIE makes no assumptions about the form, scale, or content of the data, allowing it to be used for exploratory analysis, assessment of unsupervised learning methods, data curation and quality control, data profiling, and other purposes where flexibility and scalability are paramount. We demonstrate GraviTIE on three real datasets: 500k images from the Russian misinformation dataset from Twitter, 2 million art images, and 5 million scientific figures. A screencast video is available at https://vimeo.com/310511187.

We demonstrate an unifying graph-based model for representing the infrastructure, behavior and missions of an enterprise. We introduce an algorithm for recommending resilience establishing actions based on dynamic updates to the models and show its effectiveness both through software simulation as well as live demonstration inside a cloud testbed. Our demonstrate will illustrate the effectiveness of the algorithm for preserving latency based quality of service (QoS).