Literatura científica selecionada sobre o tema "Compromis équité"
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Artigos de revistas sobre o assunto "Compromis équité"
El Amami, Hacib, e Mohamed Salah Bachta. "Recherche de compromis entre efficience, équité et protection des sols : un cas d’étude tunisien". Revue des sciences de l’eau 27, n.º 2 (13 de junho de 2014): 99–114. http://dx.doi.org/10.7202/1025561ar.
Texto completo da fonteAdanhounme, Armel Brice, e Adama Ouayiribé Traoré. "Justice (re)distributive autour d’une mine aurifère au Mali : entre légalité et équité, la recherche d’un compromis". Afrique contemporaine N° 277, n.º 1 (26 de abril de 2024): 195–220. http://dx.doi.org/10.3917/afco1.277.0195.
Texto completo da fonteLyet, Philippe, e Yvette Molina. "Épistémologie éthique dans un espace interprétatif partagé et négocié. Le cas d'une recherche conjointe québéco-française". Recherche 59, n.º 1-2 (24 de setembro de 2018): 225–41. http://dx.doi.org/10.7202/1051432ar.
Texto completo da fonteBienvenue, Louise, e Andréanne Lebrun. "Le « boulot » à Boscoville. Une expérience pédagogique auprès de la jeunesse délinquante au Québec (1949-1980)". Revue d’histoire de l’enfance « irrégulière » N° 16, n.º 1 (1 de janeiro de 2014): 111–35. http://dx.doi.org/10.3917/rhei.016.0111.
Texto completo da fonteMonange, Léa, David Busson e Florent Plassard. "Utilisation d’un accélérateur de particules pour la radiographie des ouvrages du génie civil en béton précontraint". e-journal of nondestructive testing 28, n.º 9 (setembro de 2023). http://dx.doi.org/10.58286/28489.
Texto completo da fonteTeses / dissertações sobre o assunto "Compromis équité"
Colombier, Michel. "Régulation économique et projet technique : le jeu des compromis entre efficacité, équité et innovation dans le cas de l'électrification rurale en France". Paris, EHESS, 1992. http://www.theses.fr/1992EHES0035.
Texto completo da fonteThe procedures for assisting rural electrification, in response to the imperatives of national solidarity, are base on total price equilization, wich eliminate any scope for initiating a learning process for new technologies. The economic and institutional analysis leads to the elaboration of a new framework, based on a revisited conception of equity and a transformation of the analysis of competitiveness
Alves, da Silva Guilherme. "Traitement hybride pour l'équité algorithmique". Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0323.
Texto completo da fonteAlgorithmic decisions are currently being used on a daily basis. These decisions often rely on Machine Learning (ML) algorithms that may produce complex and opaque ML models. Recent studies raised unfairness concerns by revealing discriminating outcomes produced by ML models against minorities and unprivileged groups. As ML models are capable of amplifying discrimination against minorities due to unfair outcomes, it reveals the need for approaches that uncover and remove unintended biases. Assessing fairness and mitigating unfairness are the two main tasks that have motivated the growth of the research field called {algorithmic fairness}. Several notions used to assess fairness focus on the outcomes and link to sensitive features (e.g. gender and ethnicity) through statistical measures. Although these notions have distinct semantics, the use of these definitions of fairness is criticized for being a reductionist understanding of fairness whose aim is basically to implement accept/not-accept reports, ignoring other perspectives on inequality and on societal impact. Process fairness instead is a subjective fairness notion which is centered on the process that leads to outcomes. To mitigate or remove unfairness, approaches generally apply fairness interventions in specific steps. They usually change either (1) the data before training or (2) the optimization function or (3) the algorithms' outputs in order to enforce fairer outcomes. Recently, research on algorithmic fairness have been dedicated to explore combinations of different fairness interventions, which is referred to in this thesis as {fairness hybrid-processing}. Once we try to mitigate unfairness, a tension between fairness and performance arises that is known as the fairness-accuracy trade-off. This thesis focuses on the fairness-accuracy trade-off problem since we are interested in reducing unintended biases without compromising classification performance. We thus propose ensemble-based methods to find a good compromise between fairness and classification performance of ML models, in particular models for binary classification. In addition, these methods produce ensemble classifiers thanks to a combination of fairness interventions, which characterizes the fairness hybrid-processing approaches. We introduce FixOut ({F}a{I}rness through e{X}planations and feature drop{Out}), the human-centered, model-agnostic framework that improves process fairness without compromising classification performance. It receives a pre-trained classifier (original model), a dataset, a set of sensitive features, and an explanation method as input, and it outputs a new classifier that is less reliant on the sensitive features. To assess the reliance of a given pre-trained model on sensitive features, FixOut uses explanations to estimate the contribution of features to models' outcomes. If sensitive features are shown to contribute globally to models' outcomes, then the model is deemed unfair. In this case, it builds a pool of fairer classifiers that are then aggregated to obtain an ensemble classifier. We show the adaptability of FixOut on different combinations of explanation methods and sampling approaches. We also evaluate the effectiveness of FixOut w.r.t. to process fairness but also using well-known standard fairness notions available in the literature. Furthermore, we propose several improvements such as automating the choice of FixOut's parameters and extending FixOut to other data types