How to Solve AI Bias

© 2020 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Bias in AI is a topic that impacts machine learning and artificial intelligence technology that learns from datasets and its training data. While gender discrimination and chatbots showing bias have recently caught people’s attention and imagination, the overall area of how to correct and manage bias is in its infancy for business use. Further, little is known about how to solve bias in AI and how there could potent for malicious misuse at large scale. We explore this area and propose solutions to this problem.Non peer reviewe

Understanding alternatives in data analysis activities

Data workers are non-professional data scientists who engage in data analysis activities as part of their daily work. In this position paper, we share past and on-going work to understand data workers’ sense-making practices. We use multidisciplinary approaches to explore their human-tool partnerships. We introduce our current research on the role of alternatives in data analysis activities. Finally, we conclude with open questions and research directions

An Exploratory Study on Visual Exploration of Model Simulations by Multiple Types of Experts

International audienceExperts in different domains rely increasingly on simulation models of complex processes to reach insights, make decisions, and plan future projects. These models are often used to study possible trade-offs, as experts try to optimise multiple conflicting objectives in a single investigation. Understanding all the model intricacies, however, is challenging for a single domain expert. We propose a simple approach to support multiple experts when exploring complex model results. First, we reduce the model exploration space, then present the results on a shared interactive surface, in the form of a scatterplot matrix and linked views. To explore how multiple experts analyse trade-offs using this setup, we carried out an observational study focusing on the link between expertise and insight generation during the analysis process. Our results reveal the different exploration strategies and multi-storyline approaches that domain experts adopt during trade-off analysis, and inform our recommendations for collaborative model exploration systems

Bayesian Quadrature with Prior Information: Modeling and Policies

Quadrature is the problem of estimating intractable integrals. Such integrals regularly arise in engineering and the natural sciences, especially when Bayesian methods are applied; examples include model evidences, normalizing constants and marginal distributions. This dissertation explores Bayesian quadrature, a probabilistic, model-based quadrature method. Specifically, we study different ways in which Bayesian quadrature can be adapted to account for different kinds of prior information one may have about the task. We demonstrate that by taking into account prior knowledge, Bayesian quadrature can outperform commonly used numerical methods that are agnostic to prior knowledge, such as Monte Carlo based integration. We focus on two types of information that are (a) frequently available when faced with an intractable integral and (b) can be (approximately) incorporated into Bayesian quadrature: • Natural bounds on the possible values that the integrand can take, e.g., when the integrand is a probability density function, it must nonnegative everywhere.• Knowledge about how the integral estimate will be used, i.e., for settings where quadrature is a subroutine, different downstream inference tasks can result in different priorities or desiderata for the estimate. These types of prior information are used to inform two aspects of the Bayesian quadrature inference routine: • Modeling: how the belief on the integrand can be tailored to account for the additional information.• Policies: where the integrand will be observed given a constrained budget of observations. This second aspect of Bayesian quadrature, policies for deciding where to observe the integrand, can be framed as an experimental design problem, where an agent must choose locations to evaluate a function of interest so as to maximize some notion of value. We will study the broader area of sequential experimental design, applying ideas from Bayesian decision theory to develop an efficient and nonmyopic policy for general sequential experimental design problems. We consider other sequential experimental design tasks such as Bayesian optimization and active search; in the latter, we focus on facilitating human–computer partnerships with the goal of aiding human agents engaged in data foraging through the use of active search based suggestions and an interactive visual interface. Finally, this dissertation will return to Bayesian quadrature and discuss the batch setting for experimental design, where multiple observations of the function in question are made simultaneously

Intelligence artificielle: Les défis actuels et l'action d'Inria - Livre blanc Inria

Livre blanc Inria N°01International audienceInria white papers look at major current challenges in informatics and mathematics and show actions conducted by our project-teams to address these challenges. This document is the first produced by the Strategic Technology Monitoring & Prospective Studies Unit. Thanks to a reactive observation system, this unit plays a lead role in supporting Inria to develop its strategic and scientific orientations. It also enables the institute to anticipate the impact of digital sciences on all social and economic domains. It has been coordinated by Bertrand Braunschweig with contributions from 45 researchers from Inria and from our partners. Special thanks to Peter Sturm for his precise and complete review.Les livres blancs d’Inria examinent les grands défis actuels du numérique et présentent les actions menées par noséquipes-projets pour résoudre ces défis. Ce document est le premier produit par la cellule veille et prospective d’Inria. Cette unité, par l’attention qu’elle porte aux évolutions scientifiques et technologiques, doit jouer un rôle majeur dans la détermination des orientations stratégiques et scientifiques d’Inria. Elle doit également permettre à l’Institut d’anticiper l’impact des sciences du numérique dans tous les domaines sociaux et économiques. Ce livre blanc a été coordonné par Bertrand Braunschweig avec des contributions de 45 chercheurs d’Inria et de ses partenaires. Un grand merci à Peter Sturm pour sa relecture précise et complète. Merci également au service STIP du centre de Saclay – Île-de-France pour la correction finale de la version française

Empowering users to communicate their preferences to machine learning models in Visual Analytics

Recent visual analytic (VA) systems rely on machine learning (ML) to allow users to perform a variety of data analytic tasks, e.g., biologists clustering genome samples, medical practitioners predicting the diagnosis for a new patient, ML practitioners tuning models' hyperparameter settings, etc. These VA systems support interactive construction of models to people (I call them power users) with a diverse set of expertise in ML; from non-experts, to intermediates, to expert ML users. Through my research, I designed and developed VA systems for power users empowering them to communicate their preferences to interactively construct machine learning models for their analytical tasks. In this process, I design algorithms to incorporate user interaction data in machine learning modeling pipelines. Specifically, I deployed and tested (e.g., task completion times, user satisfaction ratings, success rate in finding user-preferred models, model accuracies) two main interaction techniques, multi-model steering, and interactive objective functions to facilitate specification of user goals and objectives to underlying model(s) in VA. However, designing these VA systems for power users poses various challenges, such as addressing diversity in user expertise, metric selection, user modeling to automatically infer preferences, evaluating the success of these systems, etc. Through this work I contribute a set of VA systems that support interactive construction and selection of supervised and unsupervised models using tabular data. In addition, I also present results/findings from a design study of interactive ML in a specific domain with real users and real data.Ph.D