430 citations · 638 across the 16 of their papers we have counts for
17 papers
Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models
Xiaodan Xing, Rajat R. Rasal, Julia A. Meister +3
Counterfactual image generation answers questions about how a subject would have looked under retrospective, hypothetical scenarios. Recent methods have improved perceptual quality…
Worst-Group Equalized Odds Regularization for Multi-Attribute Fair Medical Image Classification
Nikhil Cherian Kurian, Victor Caquilpan Parra, Abin Shoby +6
Diagnostic performance in medical AI varies systematically across demographic groups, yet subgroup AUC can mask clinically important disparities. At a fixed inference-time operatin…
Enduring Disparities in the Workplace: A Pilot Study in the AI Community
Yunusa Simpa Abdulsalam, Siobhan Mackenzie Hall, Ana Quintero-Ossa +6
In efforts toward achieving responsible artificial intelligence (AI), fostering a culture of workplace transparency, diversity, and inclusion can breed innovation, trust, and emplo…
Evaluating Model Bias Requires Characterizing its Mistakes
Isabela Albuquerque, Jessica Schrouff, David Warde-Farley +3
The ability to properly benchmark model performance in the face of spurious correlations is important to both build better predictors and increase confidence that models are operat…
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
Virginia Aglietti, Ira Ktena, Jessica Schrouff +5
The sample efficiency of Bayesian optimization algorithms depends on carefully crafted acquisition functions (AFs) guiding the sequential collection of function evaluations. The be…
Mind the Graph When Balancing Data for Fairness or Robustness
Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki +5
Failures of fairness or robustness in machine learning predictive settings can be due to undesired dependencies between covariates, outcomes and auxiliary factors of variation. A c…
Adapting to Latent Subgroup Shifts via Concepts and Proxies
Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour +7
We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When thi…
Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing
Alexander Brown, Nenad Tomasev, Jan Freyberg +3
Machine learning (ML) holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. An important step is…
A Reduction to Binary Approach for Debiasing Multiclass Datasets
Ibrahim Alabdulmohsin, Jessica Schrouff, Oluwasanmi Koyejo
We propose a novel reduction-to-binary (R2B) approach that enforces demographic parity for multiclass classification with non-binary sensitive attributes via a reduction to a seque…
Disability prediction in multiple sclerosis using performance outcome measures and demographic data
Subhrajit Roy, Diana Mincu, Lev Proleev +8
Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for dis…
Healthsheet: Development of a Transparency Artifact for Health Datasets
Negar Rostamzadeh, Diana Mincu, Subhrajit Roy +7
Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role in developing ML-based healthcare systems…
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Jessica Schrouff, Natalie Harris, Oluwasanmi Koyejo +14
Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings. Importantl…
Best of both worlds: local and global explanations with human-understandable concepts
Jessica Schrouff, Sebastien Baur, Shaobo Hou +6
Interpretability techniques aim to provide the rationale behind a model's decision, typically by explaining either an individual prediction (local explanation, e.g. 'why is this pa…
Concept-based model explanations for Electronic Health Records
Diana Mincu, Eric Loreaux, Shaobo Hou +7
Recurrent Neural Networks (RNNs) are often used for sequential modeling of adverse outcomes in electronic health records (EHRs) due to their ability to encode past clinical states.…
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander D'Amour, Katherine Heller, Dan Moldovan +37
ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline i…
Inferring Javascript types using Graph Neural Networks
Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette +1
The recent use of `Big Code' with state-of-the-art deep learning methods offers promising avenues to ease program source code writing and correction. As a first step towards automa…
Interpreting weight maps in terms of cognitive or clinical neuroscience: nonsense?
Jessica Schrouff, Janaina Mourao-Miranda
Since machine learning models have been applied to neuroimaging data, researchers have drawn conclusions from the derived weight maps. In particular, weight maps of classifiers bet…