While artificial intelligence (AI)-based decision-making systems are increasingly popular, significant concerns on the potential discrimination during the AI decision-making process have been observed. For example, the distribution of predictions is usually biased and dependents on the sensitive attributes (e.g., gender and ethnicity). Numerous approaches have therefore been proposed to develop decision-making systems that are discrimination-conscious by-design, which are typically batch-based and require the simultaneous availability of all the training data for model learning. However, in the real-world, the data streams usually come on the fly which requires the model to process each input data once "on arrival" and without the need for storage and reprocessing. In addition, the data streams might also evolve over time, which further requires the model to be able to simultaneously adapt to non-stationary data distributions and time-evolving bias patterns, with an effective and robust trade-off between accuracy and fairness. In this paper, we propose a novel framework of online decision tree with fairness in the data stream with possible distribution drifting. Specifically, first, we propose two novel fairness splitting criteria that encode the data as well as possible, while simultaneously removing dependence on the sensitive attributes, and further adapts to non-stationary distribution with fine-grained control when needed. Second, we propose two fairness decision tree online growth algorithms that fulfills different online fair decision-making requirements. Our experiments show that our algorithms are able to deal with discrimination in massive and non-stationary streaming environments, with a better trade-off between fairness and predictive performance.

This article was published as a part of the Data Science Blogathon. So you have successfully built your classification model. What should you do now? How do you evaluate the performance of the model that is how good the model is in predicting the outcome. To answer these questions, let's understand the metrics used in evaluating a classification model using a simple case study.

Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored by the NLP community. Underpowered experiments make it more difficult to discern the difference between statistical noise and meaningful model improvements, and increase the chances of exaggerated findings. By meta-analyzing a set of existing NLP papers and datasets, we characterize typical power for a variety of settings and conclude that underpowered experiments are common in the NLP literature. In particular, for several tasks in the popular GLUE benchmark, small test sets mean that most attempted comparisons to state of the art models will not be adequately powered. Similarly, based on reasonable assumptions, we find that the most typical experimental design for human rating studies will be underpowered to detect small model differences, of the sort that are frequently studied. For machine translation, we find that typical test sets of 2000 sentences have approximately 75% power to detect differences of 1 BLEU point. To improve the situation going forward, we give an overview of best practices for power analysis in NLP and release a series of notebooks to assist with future power analyses.

Machine learning models in health care are often deployed in settings where it is important to protect patient privacy. In such settings, methods for differentially private (DP) learning provide a general-purpose approach to learn models with privacy guarantees. Modern methods for DP learning ensure privacy through mechanisms that censor information judged as too unique. The resulting privacy-preserving models, therefore, neglect information from the tails of a data distribution, resulting in a loss of accuracy that can disproportionately affect small groups. In this paper, we study the effects of DP learning in health care. We use state-of-the-art methods for DP learning to train privacy-preserving models in clinical prediction tasks, including x-ray classification of images and mortality prediction in time series data. We use these models to perform a comprehensive empirical investigation of the tradeoffs between privacy, utility, robustness to dataset shift, and fairness. Our results highlight lesser-known limitations of methods for DP learning in health care, models that exhibit steep tradeoffs between privacy and utility, and models whose predictions are disproportionately influenced by large demographic groups in the training data. We discuss the costs and benefits of differentially private learning in health care.

Deep neural networks (DNNs) have become increasingly popular and achieved outstanding performance in predictive tasks. However, the DNN framework itself cannot inform the user which features are more or less relevant for making the prediction, which limits its applicability in many scientific fields. We introduce neural Gaussian mirrors (NGMs), in which mirrored features are created, via a structured perturbation based on a kernel-based conditional dependence measure, to help evaluate feature importance. We design two modifications of the DNN architecture for incorporating mirrored features and providing mirror statistics to measure feature importance. As shown in simulated and real data examples, the proposed method controls the feature selection error rate at a predefined level and maintains a high selection power even with the presence of highly correlated features.

Background: Machine learning methods offer great potential for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images. In this systematic review we critically evaluate the machine learning methodologies employed in the rapidly growing literature. Methods: In this systematic review we reviewed EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for published papers and preprints uploaded from Jan 1, 2020 to June 24, 2020. Studies which consider machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images were included. A methodology quality review of each paper was performed against established benchmarks to ensure the review focusses only on high-quality reproducible papers. This study is registered with PROSPERO [CRD42020188887]. Interpretation: Our review finds that none of the developed models discussed are of potential clinical use due to methodological flaws and underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. Typically, we find that the documentation of a model's development is not sufficient to make the results reproducible and therefore of 168 candidate papers only 29 are deemed to be reproducible and subsequently considered in this review. We therefore encourage authors to use established machine learning checklists to ensure sufficient documentation is made available, and to follow the PROBAST (prediction model risk of bias assessment tool) framework to determine the underlying biases in their model development process and to mitigate these where possible. This is key to safe clinical implementation which is urgently needed.

Learning under one-sided feedback (i.e., where we only observe the labels for examples we predicted positively on) is a fundamental problem in machine learning -- applications include lending and recommendation systems. Despite this, there has been surprisingly little progress made in ways to mitigate the effects of the sampling bias that arises. We focus on generalized linear models and show that without adjusting for this sampling bias, the model may converge suboptimally or even fail to converge to the optimal solution. We propose an adaptive approach that comes with theoretical guarantees and show that it outperforms several existing methods empirically. Our method leverages variance estimation techniques to efficiently learn under uncertainty, offering a more principled alternative compared to existing approaches.

Classic decision tree learning is a binary classification algorithm that constructs models with first-class transparency - every classification has a directly derivable explanation. However, learning decision trees on modern datasets generates large trees, which in turn generate decision paths of excessive depth, obscuring the explanation of classifications. To improve the comprehensibility of classifications, we propose a new decision tree model that we call Cascading Decision Trees. Cascading Decision Trees shorten the size of explanations of classifications, without sacrificing model performance overall. Our key insight is to separate the notion of a decision path and an explanation path. Utilizing this insight, instead of having one monolithic decision tree, we build several smaller decision subtrees and cascade them in sequence. Our cascading decision subtrees are designed to specifically target explanations for positive classifications. This way each subtree identifies the smallest set of features that can classify as many positive samples as possible, without misclassifying any negative samples. Applying cascading decision trees to new samples results in a significantly shorter and succinct explanation, if one of the subtrees detects a positive classification. In that case, we immediately stop and report the decision path of only the current subtree to the user as an explanation for the classification. We evaluate our algorithm on standard datasets, as well as new real-world applications and find that our model shortens the explanation depth by over 40.8% for positive classifications compared to the classic decision tree model.

Building destruction during war is a specific form of violence which is particularly harmful to civilians, commonly used to displace populations, and therefore warrants special attention. Yet, data from war-ridden areas are typically scarce, often incomplete and highly contested, when available. The lack of such data from conflict zones severely limits media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, as well as the study of violent conflict in academic research. One approach has been to use remote sensing to identify destruction in satellite images[1]. This approach is gaining momentum as high-resolution imagery is becoming readily available and is updated ever quicker yielding weekly or even daily frequency. At the same time recent methodological advances related to deep learning have provided sophisticated tools to extract data from these images [2, 3, 4, 5].

Speaker recognition is an active research area that contains notable usage in biometric security and authentication system. Currently, there exist many well-performing models in the speaker recognition domain. However, most of the advanced models implement deep learning that requires GPU support for real-time speech recognition, and it is not suitable for low-end devices. In this paper, we propose a lightweight text-independent speaker recognition model based on random forest classifier. It also introduces new features that are used for both speaker verification and identification tasks. The proposed model uses human speech based timbral properties as features that are classified using random forest. Timbre refers to the very basic properties of sound that allow listeners to discriminate among them. The prototype uses seven most actively searched timbre properties, boominess, brightness, depth, hardness, roughness, sharpness, and warmth as features of our speaker recognition model. The experiment is carried out on speaker verification and speaker identification tasks and shows the achievements and drawbacks of the proposed model. In the speaker identification phase, it achieves a maximum accuracy of 78%. On the contrary, in the speaker verification phase, the model maintains an accuracy of 80% having an equal error rate (ERR) of 0.24.