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La présente fiche propose une revue des différents enjeux éthiques liés au développement et à l’utilisation des technologies d’int… (voir plus)elligence artificielle dans le milieu de la santé, en trois parties. D’abord, nous aborderons les enjeux éthiques liés à l’exploitation de données massives nécessaires à l’entrainement des algorithmes de l’IA. Ensuite, nous présenterons les principaux enjeux éthiques liés au développement et à l’utilisation des SIA en santé, en abordant la façon dont ces systèmes impactent nos vies ainsi que l’environnement physique et social dans lequel nous vivons. Nous présenterons finalement les principales initiatives nationales et internationales en matière d’éthique de l’IA et de la gestion des données, fruits et reflets d’une réflexion globale sur ces sujets. Ces initiatives ont notamment proposé des lignes directrices et principes normatifs servant de guides pour le développement de technologies de l’IA éthiques et responsables Il s'agit de la quatrième fiche d'une série de 4 développée dans le cadre d'un mandat réalisé pour le Ministère de la Santé et des Services sociaux du Québec (MSSS).
JANOS: An Integrated Predictive and Prescriptive Modeling Framework
David Bergman
Teng Huang
Philip Brooks
Andrea Lodi
Arvind U. Raghunathan
Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe a modeling… (voir plus) framework JANOS that seamlessly integrates the two streams of analytics, allowing researchers and practitioners to embed machine learning models in an end-to-end optimization framework. JANOS allows for specifying a prescriptive model using standard optimization modeling elements such as constraints and variables. The key novelty lies in providing modeling constructs that enable the specification of commonly used predictive models within an optimization model, have the features of the predictive model as variables in the optimization model, and incorporate the output of the predictive models as part of the objective. The framework considers two sets of decision variables: regular and predicted. The relationship between the regular and the predicted variables is specified by the user as pretrained predictive models. JANOS currently supports linear regression, logistic regression, and neural network with rectified linear activation functions. In this paper, we demonstrate the flexibility of the framework through an example on scholarship allocation in a student enrollment problem and provide a numeric performance evaluation. Summary of Contribution. This paper describes a new software tool, JANOS, that integrates predictive modeling and discrete optimization to assist decision making. Specifically, the proposed solver takes as input user-specified pretrained predictive models and formulates optimization models directly over those predictive models by embedding them within an optimization model through linear transformations.
In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings… (voir plus) suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.
In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings… (voir plus) suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.
The Role of Robotics in Achieving the United Nations Sustainable Development Goals - The Experts' Meeting at the 2021 IEEE/RSJ IROS Workshop [Industry Activities]
The development and deployment of robotic technologies can have an important role in efforts to achieve the United Nations’ (UN) Sustainab… (voir plus)le Development Goals (SDGs)—with both enabling and inhibiting impacts. During a workshop at the 2021 IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems (IROS 2021), experts from various disciplines analyzed the role of robotics in achieving the SDGs. This article provides a summary of the most important outcomes of the workshop. During the workshop panels, the variety of roles that robots can play in enabling the SDGs was underlined. The panelists discussed the challenges to the adoption of robots and to their deployment at their full potential. The probable undesirable effects of robots were also considered, and the panelists suggested approaches to correctly design SDG-relevant robotic solutions. Governance frameworks were also discussed, with respect to their contents as well as the challenges to build them. The role of military funding was briefly analyzed. Finally, several proposals for actions and policies were made. The contents of the workshop, including contributing papers and videos from the panelists, as well as additional information about future initiatives regarding robotics and the SDGs, are available at www.sustainablerobotics.org.
The Role of Robotics in Achieving the United Nations Sustainable Development Goals - The Experts' Meeting at the 2021 IEEE/RSJ IROS Workshop [Industry Activities]
