72 citations · 96 across the 18 of their papers we have counts for
7 papers · 1 filter
Is invariance all you need for algorithmic fairness? Removing demographic information can create new bias
Aditya Parikh, Eike Petersen, Stella Frank +3
Encoded demographic information in internal model representations is a commonly assumed risk factor for algorithmic bias, with demographic representation invariance often being tou…
Prevalence calibration as shortcut mitigation
Mohamed Amine Kina, Eike Petersen
Shortcut learning denotes the widespread situation in which a classifier exploits spurious correlations rather than diagnostic features. Existing mitigation strategies mostly aim t…
meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis
Dishantkumar Sutariya, Eike Petersen
Analyzing machine learning model performance stratified by patient and recording properties is becoming the accepted norm and often yields crucial insights about important model fa…
Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods
Vincent Olesen, Nina Weng, Aasa Feragen +1
Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical…
Are demographically invariant models and representations in medical imaging fair?
Eike Petersen, Enzo Ferrante, Melanie Ganz +1
Medical imaging models have been shown to encode information about patient demographics such as age, race, and sex in their latent representation, raising concerns about their pote…
On (assessing) the fairness of risk score models
Eike Petersen, Melanie Ganz, Sune Hannibal Holm +1
Recent work on algorithmic fairness has largely focused on the fairness of discrete decisions, or classifications. While such decisions are often based on risk score models, the fa…
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions
Eike Petersen, Yannik Potdevin, Esfandiar Mohammadi +8
Machine learning is expected to fuel significant improvements in medical care. To ensure that fundamental principles such as beneficence, respect for human autonomy, prevention of…