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Found 11 papers, 4 papers with code
Lazy Data Practices Harm Fairness Research
Data practices shape research and practice on fairness in machine learning (fair ML).
Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production
We employ Yung et al. (2022)'s QF4SA quality framework and present a mapping of its quality dimensions to algorithmic fairness.
Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
We introduce the term annotation sensitivity to refer to the impact of annotation data collection methods on the annotations themselves and on downstream model performance and predictions.
Bridging the Gap: Towards an Expanded Toolkit for ML-Supported Decision-Making in the Public Sector
Machine Learning (ML) systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health.
One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions
For each of these universes, we compute metrics of fairness and performance.
Uncertainty-aware predictive modeling for fair data-driven decisions
Both industry and academia have made considerable progress in developing trustworthy and responsible machine learning (ML) systems.
Fairness in Algorithmic Profiling: A German Case Study
One example is the prediction-based statistical profiling of job seekers to guide the allocation of support measures by public employment services.
Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?
The advent of powerful prediction algorithms led to increased automation of high-stake decisions regarding the allocation of scarce resources such as government spending and welfare support.
Global Trends and Predictors of Face Mask Usage During the COVID-19 Pandemic
Methods: We examined a total of 13, 723, 810 responses to a daily cross-sectional representative online survey in 38 countries who completed from April 23, 2020 to October 31, 2020 and reported having been in public at least once during the last seven days.
Applications
Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation
We cast the problem of image denoising as a domain translation problem between high and low noise domains.
A Longitudinal Framework for Predicting Nonresponse in Panel Surveys
However, predicting nonresponse in panel studies requires accounting for the longitudinal data structure in terms of model building, tuning, and evaluation.
