In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the production of entertainment). More recently, these algorithms are increasingly being used to assist socially sensitive decision-making (e.g., to decide who to admit into a degree program or to prioritize individuals for public housing). Yet, these automated tools may result in discriminative decision-making in the sense that they may treat individuals unfairly or unequally based on membership to a category or a minority, resulting in disparate treatment or disparate impact and violating both moral and ethical standards. This may happen when the training dataset is itself biased (e.g., if individuals belonging to a particular group have historically been discriminated upon). However, it may also happen when the training dataset is unbiased, if the errors made by the system affect individuals belonging to a category or minority differently (e.g., if misclassification rates for Blacks are higher than for Whites). In this paper, we unify the definitions of unfairness across classification and regression. We propose a versatile mixed-integer optimization framework for learning optimal and fair decision trees and variants thereof to prevent disparate treatment and/or disparate impact as appropriate. This translates to a flexible schema for designing fair and interpretable policies suitable for socially sensitive decision-making. We conduct extensive computational studies that show that our framework improves the state-of-the-art in the field (which typically relies on heuristics) to yield non-discriminative decisions at lower cost to overall accuracy.

Because preferences naturally arise and play an important role in many real-life decisions, they are at the backbone of various fields. In particular preferences are increasingly used in almost all matching procedures-based applications. In this work we highlight the benefit of using AI insights on preferences in a large scale application, namely the French Admission Post-Baccalaureat Platform (APB). Each year APB allocates hundreds of thousands first year applicants to universities. This is done automatically by matching applicants preferences to university seats. In practice, APB can be unable to distinguish between applicants which leads to the introduction of random selection. This has created frustration in the French public since randomness, even used as a last mean does not fare well with the republican egalitarian principle. In this work, we provide a solution to this problem. We take advantage of recent AI Preferences Theory results to show how to enhance APB in order to improve expressiveness of applicants preferences and reduce their exposure to random decisions.

Fuzzy systems have achieved great success in numerous applications. However, there are still many challenges in designing an optimal fuzzy system, e.g., how to efficiently train its parameters, how to improve its performance without adding too many parameters, how to balance the trade-off between cooperations and competitions among the rules, how to overcome the curse of dimensionality, etc. Literature has shown that by making appropriate connections between fuzzy systems and other machine learning approaches, good practices from other domains may be used to improve the fuzzy systems, and vice versa. This paper gives an overview on the functional equivalence between Takagi-Sugeno-Kang fuzzy systems and four classic machine learning approaches -- neural networks, mixture of experts, classification and regression trees, and stacking ensemble regression -- for regression problems. We also point out some promising new research directions, inspired by the functional equivalence, that could lead to solutions to the aforementioned problems. To our knowledge, this is so far the most comprehensive overview on the connections between fuzzy systems and other popular machine learning approaches, and hopefully will stimulate more hybridization between different machine learning algorithms.

The problem of learning-to-learn (LTL) [4, 30] is receiving increasing attention in recent years, due to its practical importance [11, 26] and the theoretical challenge of statistically principled and efficient solutions [1, 2, 21, 23, 9, 10, 12]. The principal aim of LTL is to design a meta-learning algorithm to select a supervised learning algorithm that is well suited to learn tasks from a prescribed family. To highlight the difference between the meta-learning algorithm and the learning algorithm, throughout the paper we will refer to the latter as the inner or within-task algorithm. The meta-algorithm is trained from a sequence of datasets, associated with different learning tasks sampled from a meta-distribution (also called environment in the literature). The performance of the selected inner algorithm is measured by the transfer risk [4, 18], that is, the average risk of the algorithm, trained on a random dataset from the same environment. A key insight is that, when the learning tasks share specific similarities, the LTL framework provides a means to leverage such similarities and select an inner algorithm of low transfer risk. In this work, we consider environments of linear regression or binary classification tasks and we assume that the associated weight vectors are all close to a common vector.

Understanding the community conditions that best support universal access and improved childhood outcomes allows ultimately to improve decision-making in the areas of planning and investment across the early stages of childhood development. Here we describe two different data-driven approaches to visualizing the lived experiences of children throughout the City of Surrey, combining data derived from both public and private sources. In one approach, we find specifically that the Early Development Instrument measuring childhood vulnerabilities across varying domains can be used to cluster neighborhoods, and that census variables can help explain similarities between neighborhoods within these clusters. In our second approach, we use program registration data from the City of Surrey's Community and Recreation Services Division. We also find a critical age of entry and exit for each program related to early childhood development and beyond, and find that certain neighborhoods and recreational programs have larger retention rates than others. This report details the journey of using data to tell the story of these neighborhoods, and provides a lens to which community initiatives can be strategically crafted through their use.

Abstract: We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Language involves a multitude of components interacting in complex ways in parallel ...

The artificial intelligence industry is often criticized for failing to think through the social repercussions of its technology--think instituting gender and racial bias in everything facial-recognition software to hiring algorithms. On Monday (March 18), Stanford University launched a new institute meant to show its commitment to addressing concerns over the industry's lack of diversity and intersectional thinking. The Institute for Human-Centered Artificial Intelligence (HAI), which plans to raise $1 billion from donors to fund its initiatives, aims to give voice to professionals from fields ranging from the humanities and the arts to education, business, engineering, and medicine, allowing them to weigh in on the future of AI. "Now is our opportunity to shape that future by putting humanists and social scientists alongside people who are developing artificial intelligence," Stanford president Marc Tessier-Lavigne declared in a press release. But in trying to address AI's blind spots, the institute has been accused of replicating its biases. Of the 121 faculty members initially announced as part of the institute, more than 100 appeared to be white, and a majority were male.

First, a couple of pointers to keep in mind when searching for datasets. Kaggle: A data science site that contains a variety of externally contributed interesting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even seattle pet licenses. Although the data sets are user-contributed, and thus have varying levels of cleanliness, the vast majority are clean. VisualData: Discover computer vision datasets by category, it allows searchable queries.

Online learning is the process of answering a sequence of questions based on the correct answers to the previous questions. It is studied in many research areas such as game theory, information theory and machine learning. There are two main components of online learning framework. First, the learning algorithm also known as the learner and second, the hypothesis class which is essentially a set of functions which learner uses to predict answers to the questions. Sometimes, this class contains some functions which have the capability to provide correct answers to the entire sequence of questions. This case is called realizable case. And when hypothesis class does not contain such functions is called unrealizable case. The goal of the learner, in both the cases, is to make as few mistakes as that could have been made by most powerful functions in hypothesis class over the entire sequence of questions. Performance of the learners is analysed by theoretical bounds on the number of mistakes made by them. This paper proposes three algorithms to improve the mistakes bound in the unrealizable case. Proposed algorithms perform highly better than the existing ones in the long run when most of the input sequences presented to the learner are likely to be realizable.

We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the machine learning literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, as well as matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics, methods that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, problems that include causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.