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Found 15 papers, 5 papers with code
Improved Text Classification via Test-Time Augmentation
Test-time augmentation -- the aggregation of predictions across transformed examples of test inputs -- is an established technique to improve the performance of image classification models.
Saliency Cards: A Framework to Characterize and Compare Saliency Methods
Moreover, with saliency cards, we are able to analyze the research landscape in a more structured fashion to identify opportunities for new methods and evaluation metrics for unmet user needs.
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs
Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models.
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs
To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them.
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.
Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making
ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gaps in their ability, but recognize signals that the system might be incorrect.
Image segmentation of liver stage malaria infection with spatial uncertainty sampling
Global eradication of malaria depends on the development of drugs effective against the silent, yet obligate liver stage of the disease.
Cultural Vocal Bursts Intensity Prediction
Image Segmentation
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A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
As machine learning (ML) increasingly affects people and society, awareness of its potential unwanted consequences has also grown.
Racial Disparities and Mistrust in End-of-Life Care
There are established racial disparities in healthcare, including during end-of-life care, when poor communication and trust can lead to suboptimal outcomes for patients and their families.
Applications
Modeling Mistrust in End-of-Life Care
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score.
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
In this work, we present a two-step framework to 1) learn relevant patient subgroups, and 2) predict an outcome for separate patient populations in a multi-task framework, where each population is a separate task.
Clinical Intervention Prediction and Understanding using Deep Networks
Real-time prediction of clinical interventions remains a challenge within intensive care units (ICUs).
The Use of Autoencoders for Discovering Patient Phenotypes
We use autoencoders to create low-dimensional embeddings of underlying patient phenotypes that we hypothesize are a governing factor in determining how different patients will react to different interventions.
Feature Representation for ICU Mortality
Results indicate that the introduced "hill" representation outperforms both the binary and linear representations, the hill representation thus has the potential to improve existing models of ICU mortality.
Autodetection and Classification of Hidden Cultural City Districts from Yelp Reviews
Topic models are a way to discover underlying themes in an otherwise unstructured collection of documents.
