Inverse Classification for Comparison-based Interpretability in Machine Learning

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data). It proposes an instance-based approach whose principle consists in determining the minimal changes needed to alter a prediction: given a data point whose classification must be explained, the proposed method consists in identifying a close neighbour classified differently, where the closeness definition integrates a sparsity constraint. This principle is implemented using observation generation in the Growing Spheres algorithm. Experimental results on two datasets illustrate the relevance of the proposed approach that can be used to gain knowledge about the classifier.Comment: preprin

Reading Between the Blurred Lines of Fisher v. University of Texas

After more than eight months of anticipation and speculation, the Supreme Court finally issued its opinion in Fisher v. University of Texas at Austin. Contrary to fears held by some and hopes held by others, the Court did not use the case as an opportunity to overrule Grutter v. Bollinger, thereby prohibiting the consideration of race in higher education admissions decisions. Instead, the Court vacated the Fifth Circuit’s decision upholding the University of Texas’ (“UT” or “University”) race-based admissions policy and remanded the case “for further proceedings consistent with [the] opinion.” At first glance, the majority opinion authored by Justice Anthony Kennedy appears to be a straight forward tutorial regarding the parameters of strict scrutiny by which courts are to examine the constitutionality of race-based admissions plans. After concluding that the Fifth Circuit failed to analyze the UT plan under the proper constitutional standard due to the deference shown to the University during its narrow tailoring analysis, the Court decided that “fairness to the litigants and the courts that heard the case requires that it be remanded so that the admissions process can be considered and judged under a correct analysis.” While the University and other affirmative action supporters may view the Court’s decision as an optimistic signpost for the future of race-based admissions policies, this Essay fears that, unfortunately, such optimism may be misplaced. It argues that a closer reading of the opinion reveals troubling language and sentiments that could detrimentally impact both the UT admissions plan, specifically, and the future of racial diversity in higher education, more broadly

Etiological Kinds

Kinds that share historical properties are dubbed “historical kinds” or “etiological kinds,” and they have some distinctive features. I will try to characterize etiological kinds in general terms a..

Learning and Interpreting Multi-Multi-Instance Learning Networks

We introduce an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e.g., a document could be represented as a bag of sentences, which in turn are bags of words). This framework can be useful in various scenarios, such as text and image classification, but also supervised learning over graphs. As a further advantage, multi-multi instance learning enables a particular way of interpreting predictions and the decision function. Our approach is based on a special neural network layer, called bag-layer, whose units aggregate bags of inputs of arbitrary size. We prove theoretically that the associated class of functions contains all Boolean functions over sets of sets of instances and we provide empirical evidence that functions of this kind can be actually learned on semi-synthetic datasets. We finally present experiments on text classification, on citation graphs, and social graph data, which show that our model obtains competitive results with respect to accuracy when compared to other approaches such as convolutional networks on graphs, while at the same time it supports a general approach to interpret the learnt model, as well as explain individual predictions.Comment: JML

Ownership structures and the leverage of listed firms in China

In this paper the relationship between leverage, performance and a firm’s ownership structure is investigated. It is an exploratory study based on listed firms in China, that is all firms listed on the Shanghai and Shenzhen stock exchanges from 1999 to 2005. The results of an empirical analysis of ownership structures and the leverage are reported in this paper. The most significant result is that foreign holdings are found to have a significant relationship with the leverage of listed firms in China. Whereas, somewhat unexpectedly, institutional ownership, through Legal Person holding companies, state ownership and private holdings are not found to have a significant relationship with the capital structure choices of firms in China. The results also suggest that some firm-specific factors that are relevant for explaining firm leverage generally referred to in studies in developed economies, such as profitability, growth opportunities, size and tax shields, are also relevant in China. The age of the firms and the industry to which they principally belong also has significant bearing. Yet direct government grants and the use of an internationally renowned auditing firm do not show a significant relationship

Natural Kinds in Evolution and Systematics: Metaphysical and Epistemological Considerations

Despite the traditional focus on metaphysical issues in discussions of natural kinds in biology, epistemological considerations are at least as important. By revisiting the debate as to whether taxa are kinds or individuals, I argue that both accounts are metaphysically compatible but one or the other approach can be pragmatically preferable depending on the epistemic context. Recent objections against construing species as homeostatic property cluster kinds are also addressed. The second part of the paper broadens the perspective by considering homologues as another example of natural kinds, comparing them with analogues as functionally defined kinds. Given that there are various types of natural kinds, I discuss the different theoretical purposes served by diverse kind concepts, suggesting that there is no clear-cut distinction between natural kinds and other kinds, such as functional kinds. Rather than attempting to offer a unique metaphysical account of ‘natural’ kind, a more fruitful approach consists in the epistemological study of how different natural kind concepts are employed in scientific reasoning