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Found 11 papers, 3 papers with code
Adapting to Latent Subgroup Shifts via Concepts and Proxies
We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target.
Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing
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.
A Reduction to Binary Approach for Debiasing Multiclass Datasets
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 sequence of binary debiasing tasks.
Disability prediction in multiple sclerosis using performance outcome measures and demographic data
To the best of our knowledge, our results are the first to show that it is possible to predict disease progression using POMs and demographic data in the context of both clinical trials and smartphone-base studies by using two datasets.
Healthsheet: Development of a Transparency Artifact for Health Datasets
Our findings from the interviewee study and case studies show 1) that datasheets should be contextualized for healthcare, 2) that despite incentives to adopt accountability practices such as datasheets, there is a lack of consistency in the broader use of these practices 3) how the ML for health community views datasheets and particularly \textit{Healthsheets} as diagnostic tool to surface the limitations and strength of datasets and 4) the relative importance of different fields in the datasheet to healthcare concerns.
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings.
Best of both worlds: local and global explanations with human-understandable concepts
While there are many methods focused on either one, few frameworks can provide both local and global explanations in a consistent manner.
Concept-based model explanations for Electronic Health Records
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
Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains.
Inferring Javascript types using Graph Neural Networks
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.
Interpreting weight maps in terms of cognitive or clinical neuroscience: nonsense?
Since machine learning models have been applied to neuroimaging data, researchers have drawn conclusions from the derived weight maps.
