Publications

Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults
Charlene H Chu
Rune Nyrup
Kathleen Leslie
Jiamin Shi
Andria Bianchi
Alexandra Lyn
Molly McNicholl
Shehroz S Khan
Samira Abbasgholizadeh-Rahimi
Amanda Grenier
Abstract Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, edu… (voir plus)cation, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.
2022-01-20
The Gerontologist (publié)
doi.org
Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults
Charlene H Chu
Rune Nyrup
Kathleen Leslie
Jiamin Shi
Andria Bianchi
Alexandra Lyn
Molly McNicholl
Shehroz S Khan
Samira Abbasgholizadeh-Rahimi
A. Grenier
Abstract Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, edu… (voir plus)cation, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.
2022-01-20
The gerontologist (published)
doi.org
Bayesian stroke modeling details sex biases in the white matter substrates of aphasia
Julius M. Kernbach
Gesa Hartwigsen
Jae‐Sung Lim
Hee-Joon Bae
Kyung‐Ho Yu
Gottfried Schlaug
Anna K. Bonkhoff
Natalia S. Rost
Danilo Bzdok
2022-01-18
bioRxiv (preprint)
doi.org
Invariant representation driven neural classifier for anti-QCD jet tagging
Taoli Cheng
Aaron Courville
2022-01-18
ArXiv (preprint)
doi.org
arxiv.org
Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for Evaluation and Rehabilitation of Mobility Across Continuums of Care
Sara Ahmed
P. Archambault
Claudine Auger
Audrey Durand
Joyce Phua Pau Fung
Eva Kehayia
Anouk Lamontagne
Annette Majnemer
Sylvie Nadeau
Joelle Pineau
Alain Ptito
B. Swaine
Background Rapid advances in technologies over the past 10 years have enabled large-scale biomedical and psychosocial rehabilitation researc… (voir plus)h to improve the function and social integration of persons with physical impairments across the lifespan. The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies (BRILLIANT) in community mobility rehabilitation aims to generate evidence-based research to improve rehabilitation for individuals with acquired brain injury (ABI). Objective This study aims to (1) identify the factors limiting or enhancing mobility in real-world community environments (public spaces, including the mall, home, and outdoors) and understand their complex interplay in individuals of all ages with ABI and (2) customize community environment mobility training by identifying, on a continuous basis, the specific rehabilitation strategies and interventions that patient subgroups benefit from most. Here, we present the research and technology plan for the BRILLIANT initiative. Methods A cohort of individuals, adults and children, with ABI (N=1500) will be recruited. Patients will be recruited from the acute care and rehabilitation partner centers within 4 health regions (living labs) and followed throughout the continuum of rehabilitation. Participants will also be recruited from the community. Biomedical, clinician-reported, patient-reported, and brain imaging data will be collected. Theme 1 will implement and evaluate the feasibility of collecting data across BRILLIANT living labs and conduct predictive analyses and artificial intelligence (AI) to identify mobility subgroups. Theme 2 will implement, evaluate, and identify community mobility interventions that optimize outcomes for mobility subgroups of patients with ABI. Results The biomedical infrastructure and equipment have been established across the living labs, and development of the clinician- and patient-reported outcome digital solutions is underway. Recruitment is expected to begin in May 2022. Conclusions The program will develop and deploy a comprehensive clinical and community-based mobility-monitoring system to evaluate the factors that result in poor mobility, and develop personalized mobility interventions that are optimized for specific patient subgroups. Technology solutions will be designed to support clinicians and patients to deliver cost-effective care and the right intervention to the right person at the right time to optimize long-term functional potential and meaningful participation in the community. International Registered Report Identifier (IRRID) PRR1-10.2196/12506
2022-01-13
JMIR Research Protocols (published)
doi.org
Biomedical Research & Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for a longitudinal evaluation of mobility outcomes (Preprint)
Sara Ahmed
Philippe Archambault
Claudine Auger
Audrey Durand
Joyce Fung
Eva Kehayia
Anouk Lamontagne
Annette Majnemer
Sylvie Nadeau
Joelle Pineau
Alain Ptito
Bonnie Swaine
UNSTRUCTURED The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Re… (voir plus)habilitation (BRILLIANT) program to provide evidence-based research to improve rehabilitation for individuals with Acquired Brain Injury (ABI: traumatic brain injury [TBI], cerebral palsy-fetal/perinatal brain injury, and stroke). The vision of the BRILLIANT program is to optimize mobility of persons with ABI across the lifespan. The program will develop and deploy a comprehensive clinical and community based mobility monitoring system to evaluate the factors that result in poor mobility, and develop personalized mobility interventions that are optimized for specific patient sub-groups. These innovations will be used by front-line clinicians to deliver cost-effective care; the right intervention to the right person at the right time, accounting for long-term functional potential and meaningful participation in the community.
2022-01-13
(published)
doi.org
Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation
Yuyan Chen
Yanghua Xiao
Bang Liu
Interpreting the predictions of existing Question Answering (QA) models is critical to many real-world intelligent applications, such as QA … (voir plus)systems for healthcare, education, and finance. However, existing QA models lack interpretability and provide no feedback or explanation for end-users to help them understand why a specific prediction is the answer to a question. In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models. Unlike previous research that simply extracts several sentence(s) in the context as evidence, we are the first to explicitly define the concept of evidence as the supporting facts in a context which are informative, concise, and readable. Besides, we provide effective strategies to quantitatively measure the informativeness, conciseness and readability of evidence. Furthermore, we propose Grow-and-Clip Evidence Distillation (GCED) algorithm to extract evidences from the contexts by trade-off informativeness, conciseness, and readability. We conduct extensive experiments on the SQuAD and TriviaQA datasets with several baseline models to evaluate the effect of GCED on interpreting answers to questions. Human evaluation are also carried out to check the quality of distilled evidences. Experimental results show that automatic distilled evidences have human-like informativeness, conciseness and readability, which can enhance the interpretability of the answers to questions.
2022-01-13
ArXiv (preprint)
doi.org
arxiv.org
Robust Contrastive Learning against Noisy Views
Ching-Yao Chuang
(Rex) Devon Hjelm
Xin Wang
Vibhav Vineet
Neel Joshi
Antonio Torralba
Stefanie Jegelka
Ya-heng Song
Contrastive learning relies on an assumption that positive pairs contain related views that share certain underlying information about an in… (voir plus)stance, e.g., patches of an image or co-occurring multimodal signals of a video. What if this assumption is violated? The literature suggests that contrastive learning produces suboptimal representations in the presence of noisy views, e.g., false positive pairs with no apparent shared information. In this work, we pro-pose a new contrastive loss function that is robust against noisy views. We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure. The proposed loss is completely modality-agnostic and a simple drop-in replacement for the InfoNCE loss, which makes it easy to apply to ex-isting contrastive frameworks. We show that our approach provides consistent improvements over the state-of-the-art on image, video, and graph contrastive learning bench-marks that exhibit a variety of real-world noise patterns.
2022-01-12
ArXiv (preprint)
doi.org
arxiv.org
Magnetoencephalography resting-state correlates of executive and language components of verbal fluency
Victor Oswald
Younes Zerouali
Aubrée Boulet-Craig
Maja Krajinovic
Caroline Laverdière
Daniel Sinnett
Pierre Jolicoeur
Sarah Lippé
Karim Jerbi
Philippe Robaey
2022-01-10
Scientific Reports (publié)
doi.org
Automated, interactive, and traceable domain modelling empowered by artificial intelligence
Rijul Saini
Gunter Mussbacher
Jin Guo
Jörg Kienzle
2022-01-08
Software and Systems Modeling (publié)
doi.org
FIXME: synchronize with database! An empirical study of data access self-admitted technical debt
Biruk Asmare Muse
Csaba Zoltán Nagy
Anthony Cleve
Foutse Khomh
Giuliano Antoniol
2022-01-06
ArXiv (preprint)
doi.org
arxiv.org
A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions
Anthony GX-Chen
Veronica Chelu
Blake Richards
Joelle Pineau
Estimating value functions is a core component of reinforcement learning algorithms. Temporal difference (TD) learning algorithms use bootst… (voir plus)rapping, i.e. they update the value function toward a learning target using value estimates at subsequent time-steps. Alternatively, the value function can be updated toward a learning target constructed by separately predicting successor features (SF)—a policy-dependent model—and linearly combining them with instantaneous rewards. We focus on bootstrapping targets used when estimating value functions, and propose a new backup target, the ?-return mixture, which implicitly combines value-predictive knowledge (used by TD methods) with (successor) feature-predictive knowledge—with a parameter ? capturing how much to rely on each. We illustrate that incorporating predictive knowledge through an ??-discounted SF model makes more efficient use of sampled experience, compared to either extreme, i.e. bootstrapping entirely on the value function estimate, or bootstrapping on the product of separately estimated successor features and instantaneous reward models. We empirically show this approach leads to faster policy evaluation and better control performance, for tabular and nonlinear function approximations, indicating scalability and generality.
2022-01-05
ArXiv (preprint)
doi.org
arxiv.org