Assessing the diversity of a dataset of information associated with people is crucial before using such data for downstream applications. For a given dataset, this often involves computing the imbalance or disparity in the empirical marginal distribution of a protected attribute (e.g. gender, dialect, etc.). However, real-world datasets, such as images from Google Search or collections of Twitter posts, often do not have protected attributes labeled. Consequently, to derive disparity measures for such datasets, the elements need to hand-labeled or crowd-annotated, which are expensive processes. We propose a cost-effective approach to approximate the disparity of a given unlabeled dataset, with respect to a protected attribute, using a control set of labeled representative examples. Our proposed algorithm uses the pairwise similarity between elements in the dataset and elements in the control set to effectively bootstrap an approximation to the disparity of the dataset. Importantly, we show that using a control set whose size is much smaller than the size of the dataset is sufficient to achieve a small approximation error. Further, based on our theoretical framework, we also provide an algorithm to construct adaptive control sets that achieve smaller approximation errors than randomly chosen control sets. Simulations on two image datasets and one Twitter dataset demonstrate the efficacy of our approach (using random and adaptive control sets) in auditing the diversity of a wide variety of datasets.

The study of fairness in multiwinner elections focuses on settings where candidates have attributes. However, voters may also be divided into predefined populations under one or more attributes (e.g., "California" and "Illinois" populations under the "state" attribute), which may be same or different from candidate attributes. The models that focus on candidate attributes alone may systematically under-represent smaller voter populations. Hence, we develop a model, DiRe Committee Winner Determination (DRCWD), which delineates candidate and voter attributes to select a committee by specifying diversity and representation constraints and a voting rule. We show the generalizability of our model, and analyze its computational complexity, inapproximability, and parameterized complexity. We develop a heuristic-based algorithm, which finds the winning DiRe committee in under two minutes on 63% of the instances of synthetic datasets and on 100% of instances of real-world datasets. We present an empirical analysis of the running time, feasibility, and utility traded-off. Overall, DRCWD motivates that a study of multiwinner elections should consider both its actors, namely candidates and voters, as candidate-specific "fair" models can unknowingly harm voter populations, and vice versa. Additionally, even when the attributes of candidates and voters coincide, it is important to treat them separately as having a female candidate on the committee, for example, is different from having a candidate on the committee who is preferred by the female voters, and who themselves may or may not be female.

Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations and development cycle. We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects. The framework is illustrated by applying it to a healthcare application. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs. The pairing of conceptual modeling and machine learning in this this way should help lay the foundations for future research.

"Does this count as an'act of God'?" That's the question legal teams everywhere were asking when Covid hit the US last March. Clients wanted to search out force majeure clauses in thousands of real estate agreements and other contracts, wondering if a pandemic could render them null and void. Not only were there more docs to review than usual, but also lawyers had to find quick answers to critical, unprecedented issues...all while working from home. The 100-person team at Luminance, a UK-based AI-for-legal startup, felt like they were at the center of it all. Founded by University of Cambridge mathematicians in 2015, the company specializes in automated legal doc review and analysis.

The plurality of information accessible to internet users from all generations provides a sense of digital connection, a transcendent relationship that is perhaps considered unprecedented in human history. Within our society, there are precedents of specific industries and companies that have failed to scale into the world of digital transformation. For example, Canada's own Blackberry (formerly known as Research in Motion), transitioned from a smartphone manufacturer to artificial intelligence (AI) based cyber-security solution software company. The lessons showcased that because the world is changing so quickly, various fields are inherently forced to adapt to the technological advancements of processes and methods. The cruel reality is that industries that fail to transition successfully into an era of digital transformation may cease to exist. The legal industry has been quite consistent with its traditional values of the law, and so its methods of teaching have not changed in comparison with other highly regulated professions like medicine.

I can't tell you how excited I am to have you here with me, and I hope between us we can build not just a newsletter, but a news community. Sometimes there's a story that just sums up all the hopes and fears of its entire field. GitHub is a platform that lets developers collaborate on coding with colleagues, friends and strangers around the world, and host the results. Owned by Microsoft since 2018, the site is the largest host of source code in the world, and a crucial part of many companies' digital infrastructure. Late last month, GitHub launched a new AI tool, called Copilot.

The promise of an economic boost via machine learning is understandably seductive, and private and government actors are now regularly using AI in …

Forgetting - or variable elimination - is an operation that allows the removal, from a knowledge base, of middle variables no longer deemed relevant. In recent years, many different approaches for forgetting in Answer Set Programming have been proposed, in the form of specific operators, or classes of such operators, commonly following different principles and obeying different properties. Each such approach was developed to somehow address some particular view on forgetting, aimed at obeying a specific set of properties deemed desirable in such view, but a comprehensive and uniform overview of all the existing operators and properties is missing. In this paper, we thoroughly examine existing properties and (classes of) operators for forgetting in Answer Set Programming, drawing a complete picture of the landscape of these classes of forgetting operators, which includes many novel results on relations between properties and operators, including considerations on concrete operators to compute results of forgetting and computational complexity. Our goal is to provide guidance to help users in choosing the operator most adequate for their application requirements.

As more and more companies adopt AI, a question arises--can the federal government keep up with all the changes from a regulatory perspective? In some cases, the federal government is behind (see Why Are Technology Companies Quitting Facial Recognition?). In a recent blog post, the FTC has warned companies that they have sufficient laws to enforce truth, fairness, and equity when enforcing the developers and users of AI. The FTC essentially says that companies need to hold themselves accountable for their AI, or the FTC will take enforcement action against them. The FTC's primary focus is on consumer protection.

I don't suppose you're the kind of person who, amid a pandemic and the Euros, generally spends Monday morning wondering about the latest ruminations of Judge James Boasberg of Washington, DC. I'm paid to know about these things, and I'd never heard of him. Until, that is, a fortnight ago, when he issued a legal opinion with such potentially profound implications for you, me, and the epoch into which we have stumbled that suddenly I took a rather close interest in this estimable man of law. The learned judge dismissed two anti-trust cases against Facebook, one from the Federal Trade Commission and another from an alliance of US states. They had argued that the social media giant is in effect a monopoly.