Supplier’s Declaration of Conformity (SDoC) 
The accuracy and reliability of machine learning algorithms are an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety, security, and provenance, are also critical elements to engender consumers’ trust in a service. In this paper, we propose a supplier’s declaration of conformity (SDoC) for AI services to help increase trust in AI services. An SDoC is a transparent, standardized, but often not legally required, document used in many industries and sectors to describe the lineage of a product along with the safety and performance testing it has undergone. We envision an SDoC for AI services to contain purpose, performance, safety, security, and provenance information to be completed and voluntarily released by AI service providers for examination by consumers. Importantly, it conveys product-level rather than component-level functional testing. We suggest a set of declaration items tailored to AI and provide examples for two fictitious AI services. …
Squared-Loss Mutual Information Regularization (SMIR)
We propose squared-loss mutual information regularization (SMIR) for multi-class probabilistic classi cation, following the information maximization principle. SMIR is convex under mild conditions and thus improves the nonconvexity of mutual information regularization. It offers all of the following four abilities to semi-supervised algorithms: Analytical solution, out-of-sample/multi-class classification, and probabilistic output. Furthermore, novel generalization error bounds are derived. Experiments show SMIR compares favorably with state-of-the-art methods. …
Network Maximal Correlation (NMC)
We introduce Network Maximal Correlation (NMC) as a multivariate measure of nonlinear association among random variables. NMC is defined via an optimization that infers (non-trivial) transformations of variables by maximizing aggregate inner products between transformed variables. We characterize a solution of the NMC optimization using geometric properties of Hilbert spaces for finite discrete and jointly Gaussian random variables. For finite discrete variables, we propose an algorithm based on alternating conditional expectation to determine NMC. We also show that empirically computed NMC converges to NMC exponentially fast in sample size. For jointly Gaussian variables, we show that under some conditions the NMC optimization is an instance of the Max-Cut problem. We then illustrate an application of NMC and multiple MC in inference of graphical model for bijective, possibly non-monotone, functions of jointly Gaussian variables generalizing the copula setup developed by Liu et al. Finally, we illustrate NMC’s utility in a real data application of learning nonlinear dependencies among genes in a cancer dataset. …
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