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Found 9 papers, 6 papers with code
Mitigating attribute amplification in counterfactual image generation
Causal generative modelling is gaining interest in medical imaging due to its ability to answer interventional and counterfactual queries.
Synthia's Melody: A Benchmark Framework for Unsupervised Domain Adaptation in Audio
Despite significant advancements in deep learning for vision and natural language, unsupervised domain adaptation in audio remains relatively unexplored.
No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging
As machine learning methods gain prominence within clinical decision-making, addressing fairness concerns becomes increasingly urgent.
The Role of Subgroup Separability in Group-Fair Medical Image Classification
We investigate performance disparities in deep classifiers.
Risk of Bias in Chest Radiography Deep Learning Foundation Models
Purpose: To analyze a recently published chest radiography foundation model for the presence of biases that could lead to subgroup performance disparities across biological sex and race.
Structured Uncertainty in the Observation Space of Variational Autoencoders
In contrast, improving the model for the observational distribution is rarely considered and typically defaults to a pixel-wise independent categorical or normal distribution.
Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms
The models consistently yield higher FPR on subgroups known to be historically underserved, and the study concludes that the models exhibit and potentially even amplify systematic underdiagnosis.
Algorithmic encoding of protected characteristics in image-based models for disease detection
We explore test set resampling, transfer learning, multitask learning, and model inspection to assess the relationship between the encoding of protected characteristics and disease detection performance across subgroups.
An Event-Driven Approach to Serverless Seismic Imaging in the Cloud
As an alternative to the generic lift and shift approach, we consider the specific application of seismic imaging and demonstrate a serverless and event-driven approach for running large-scale instances of this problem in the cloud.
Distributed, Parallel, and Cluster Computing Geophysics
