Can we trust black-box machine learning with its decisions? Can we trust algorithms to train machine learning models on sensitive data? Transparency and privacy are two fundamental elements of trust for adopting machine learning. In this paper, we investigate the relation between interpretability and privacy. In particular we analyze if an adversary can exploit transparent machine learning to infer sensitive information about its training set. To this end, we perform membership inference as well as reconstruction attacks on two popular classes of algorithms for explaining machine learning models: feature-based and record-based influence measures. We empirically show that an attacker, that only observes the feature-based explanations, has the same power as the state of the art membership inference attacks on model predictions. We also demonstrate that record-based explanations can be effectively exploited to reconstruct significant parts of the training set. Finally, our results indicate that minorities and special cases are more vulnerable to these type of attacks than majority groups.

Artificial Intelligence (AI) study and use is on the rise. Tools to enable AI are becoming more readily available, simpler to use and easier to implement. What's more is that the definition of AI itself has been broken down into ingredients that, when later applied into a recipe (or process), can provide multiple desired outcomes. One of the more important ingredients used in most recipes is Machine Learning. Machine Learning in essence is a way of teaching computers to provide more accurate predictions on provided data.

The modelDown package turns classification or regression models into HTML static websites. With one command you can convert one or more models into a website with visual and tabular model summaries. So it's model agnostic (feel free to combine random forest with glm), easy to extend and parameterise. Here you can browse an example website automatically created for 4 classification models (random forest, gradient boosting, support vector machines, k-nearest neighbours). The R code beyond this example is here.

Researchers at the University of Washington created a tool, which could potentially be developed into an application for smart speakers and smartphones, that uses algorithms and machine learning to identify instances of agonal breathing, a sign of cardiac arrest, with an accuracy of 97% at distances of up to 6 meters away. A contactless support vector machine (SVM), an artificial intelligence system that uses algorithms and machine learning, could be used by smart speakers and similar devices to detect agonal breathing, a symptom of potential cardiac arrest. The machine performs with 97% accuracy from a distance of up to 6 meters away, according to a study in Nature Partner Journals Digital Medicine. "A lot of people have smart speakers in their homes, and these devices have amazing capabilities that we can take advantage of," said sudy co-author Shyam Gollakota, PhD, associate professor at the University of Washington's Paul G. Allen School of Computer Science and Engineering, in a statement. "We envision a contactless system that works by continuously and passively monitoring the bedroom for an agonal breathing event, and alerts anyone nearby to come provide CPR. And then if there's no response, the device can automatically call 911."

Interaction function (IFC), which captures interactions among items and users, is of great importance in collaborative filtering (CF). The inner product is the most popular IFC due to its success in low-rank matrix factorization. However, interactions in real-world applications can be highly complex. Many other operations (such as plus and concatenation) have also been proposed, and can possibly offer better performance than the inner product. In this paper, motivated by the success of automated machine learning, we propose to search for proper interaction functions (SIF) for CF tasks. We first design an expressive search space for SIF by reviewing and generalizing existing CF approaches. We then propose to represent the search space as a structured multi-layer perceptron, and design a stochastic gradient descent algorithm which can simultaneously update both architectures and learning parameters. Experimental results demonstrate that the proposed method can be much more efficient than popular AutoML approaches, and also obtain much better prediction performance than state-of-the-art CF approaches.

Machine learning (ML) models trained by differentially private stochastic gradient descent (DP-SGD) has much lower utility than the non-private ones. To mitigate this degradation, we propose a DP Laplacian smoothing SGD (DP-LSSGD) for privacy-preserving ML. At the core of DP-LSSGD is the Laplace smoothing operator, which smooths out the Gaussian noise vector used in the Gaussian mechanism. Under the same amount of noise used in the Gaussian mechanism, DP-LSSGD attains the same differential privacy guarantee, but a strictly better utility guarantee, excluding an intrinsic term which is usually dominated by the other terms, for convex optimization than DP-SGD by a factor which is much less than one. In practice, DP-LSSGD makes training both convex and nonconvex ML models more efficient and enables the trained models to generalize better. For ResNet20, under the same strong differential privacy guarantee, DP-LSSGD can lift the testing accuracy of the trained private model by more than $8$\% compared with DP-SGD. The proposed algorithm is simple to implement and the extra computational complexity and memory overhead compared with DP-SGD are negligible. DP-LSSGD is applicable to train a large variety of ML models, including deep neural nets. The code is available at \url{https://github.com/BaoWangMath/DP-LSSGD}.

In this paper we prove that, in the deep limit, the stochastic gradient descent on a ResNet type deep neural network, where each layer share the same weight matrix, converges to the stochastic gradient descent for a Neural ODE and that the corresponding value/loss functions converge. Our result gives, in the context of minimization by stochastic gradient descent, a theoretical foundation for considering Neural ODEs as the deep limit of ResNets. Our proof is based on certain decay estimates for associated Fokker-Planck equations.

Matrix factorization methods are extensively employed to understand complex data. In this paper, we introduce the cross-product penalized component analysis (XCAN), a sparse matrix factorization based on the optimization of a loss function that allows a trade-off between variance maximization and structural preservation. The approach is based on previous developments, notably (i) the Sparse Principal Component Analysis (SPCA) framework based on the LASSO, (ii) extensions of SPCA to constrain both modes of the factorization, like co-clustering or the Penalized Matrix Decomposition (PMD), and (iii) the Group-wise Principal Component Analysis (GPCA) method. The result is a flexible modeling approach that can be used for data exploration in a large variety of problems. We demonstrate its use with applications from different disciplines.

As artificial intelligence (AI) systems permeate our world, the problem of implicit biases in these systems have become more serious. AI systems are routinely used to make decisions or support the decision-making process in credit, hiring, criminal justice, and education, all of which are domains protected by anti-discrimination law. Although AI systems appear to eliminate the biases of a human decision maker, they may perpetuate or even exacerbate biases in the training data [64]. Such biases are especially objectionable when it adversely affects underprivileged groups of users [3]. Although the most obvious remedy is to remove the biases in the training data, this is impractical in most applications.