Determinants of Access to Essential Surgery in the Democratic Republic of Congo
Luc Malemo Kalisya
Ava Yap
Boniface Mitume
Christian Salmon
Kambale Karafuli
Rosebella Onyango
Differential and overlapping effects between exogenous and endogenous attention shape perceptual facilitation during visual processing
Mathieu Landry
Jason da Silva Castanheira
Learning Neural Implicit Representations with Surface Signal Parameterizations
Yanran Guan
Andrei Chubarau
Ruby Rao
Machine learning-assisted selection of adsorption-based carbon dioxide capture materials
Eslam G. Al-Sakkari
Ahmed Ragab
Terry M.Y. So
Marzieh Shokrollahi
Philippe Navarri
Ali Elkamel
Mouloud Amazouz
Neural efficiency in an aviation task with different levels of difficulty: Assessing different biometrics during a performance task
Andrew Law
Sergio Mejia Romero
Sion Jennings
Jocelyn Faubert
Neural representation of occluded objects in visual cortex
Courtney Mansfield
Tim Kietzmann
Jasper JF van den Bosch
Marieke Mur
Nikolaus Kriegeskorte
Fraser Smith
Reconstructing mental images using Bubbles and electroencephalography
Audrey Lamy-Proulx
Jasper JF van den Bosch
Catherine Landry
Peter Brotherwood
Vincent Taschereau-Dumouchel
Frédéric Gosselin
The Different Faces of AI Ethics Across the World: A Principle-to-Practice Gap Analysis
Lionel Nganyewou Tidjon
Artificial Intelligence (AI) is transforming our daily life with many applications in healthcare, space exploration, banking, and finance. T… (voir plus)his rapid progress in AI has brought increasing attention to the potential impacts of AI technologies on society, with ethically questionable consequences. In recent years, several ethical principles have been released by governments, national organizations, and international organizations. These principles outline high-level precepts to guide the ethical development, deployment, and governance of AI. However, the abstract nature, diversity, and context-dependence of these principles make them difficult to implement and operationalize, resulting in gaps between principles and their execution. Most recent work analyzed and summarized existing AI principles and guidelines but did not provide findings on principle-to-practice gaps nor how to mitigate them. These findings are particularly important to ensure that AI practical guidances are aligned with ethical principles and values. In this article, we provide a contextual and global evaluation of current ethical AI principles for all continents, with the aim to identify potential principle characteristics tailored to specific countries or applicable across countries. Next, we analyze the current level of AI readiness and current practical guidances of ethical AI principles in different countries, to identify gaps in the practical guidance of AI principles and their causes. Finally, we propose recommendations to mitigate the principle-to-practice gaps.
The semantic distance between a linguistic prime and a natural scene target predicts reaction times in a visual search experiment
Katerina Marie Simkova
Jasper JF van den Bosch
Damiano Grignolio
Clayton Hickey
Variational Nested Dropout
Yufei Cui
Yu Mao
Ziquan Liu
Qiao Li
Antoni B. Chan
Tei-Wei Kuo
Chun Jason Xue
Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance du… (voir plus)ring training. It has been explored for: I. Constructing nested nets Cui et al. 2020, Cui et al. 2021: the nested nets are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. The nested dropout implicitly ranks the network parameters, generating a set of sub-networks such that any smaller sub-network forms the basis of a larger one. II. Learning ordered representation Rippel et al. 2014: the nested dropout applied to the latent representation of a generative model (e.g., auto-encoder) ranks the features, enforcing explicit order of the dense representation over dimensions. However, the dropout rate is fixed as a hyper-parameter during the whole training process. For nested nets, when network parameters are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data. For generative models, the importance of features is specified as a constant vector, restraining the flexibility of representation learning. To address the problem, we focus on the probabilistic counterpart of the nested dropout. We propose a variational nested dropout (VND) operation that draws samples of multi-dimensional ordered masks at a low cost, providing useful gradients to the parameters of nested dropout. Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the parameter distributions. We further exploit the VND under different generative models for learning ordered latent distributions. In experiments, we show that the proposed approach outperforms the nested network in terms of accuracy, calibration, and out-of-domain detection in classification tasks. It also outperforms the related generative models on data generation tasks.
Do visual mental imagery and exteroceptive perception rely on the same mechanisms?
Catherine Landry
Jasper JF van den Bosch
Frédéric Gosselin
Vincent Taschereau-Dumouchel
Multivariate analytical approaches for investigating brain-behavior relationships
E. Leighton Durham
Karam Ghanem
Andrew J. Stier
Carlos Cardenas-Iniguez
Gabrielle E. Reimann
Hee Jung Jeong
Randolph M. Dupont
Xiaoyu Dong
Tyler M. Moore
Marc G. Berman
Benjamin B. Lahey
Antonia N. Kaczkurkin