A classification of feedback loops and their relation to biases in automated decision-making systems
Prediction-based decision-making systems are becoming increasingly prevalent in various
domains. Previous studies have demonstrated that such systems are vulnerable to runaway …
domains. Previous studies have demonstrated that such systems are vulnerable to runaway …
Bias on demand: a modelling framework that generates synthetic data with bias
Nowadays, Machine Learning (ML) systems are widely used in various businesses and are
increasingly being adopted to make decisions that can significantly impact people's lives …
increasingly being adopted to make decisions that can significantly impact people's lives …
To be forgotten or to be fair: Unveiling fairness implications of machine unlearning methods
The right to be forgotten (RTBF) allows individuals to request the removal of personal
information from online platforms. Researchers have proposed machine unlearning …
information from online platforms. Researchers have proposed machine unlearning …
Fairness and risk: an ethical argument for a group fairness definition insurers can use
Algorithmic predictions are promising for insurance companies to develop personalized risk
models for determining premiums. In this context, issues of fairness, discrimination, and …
models for determining premiums. In this context, issues of fairness, discrimination, and …
Consensus and subjectivity of skin tone annotation for ml fairness
C Schumann, F Olanubi, A Wright… - Advances in …, 2024 - proceedings.neurips.cc
Understanding different human attributes and how they affect model behavior may become
a standard need for all model creation and usage, from traditional computer vision tasks to …
a standard need for all model creation and usage, from traditional computer vision tasks to …
Perspectives of Defining Algorithmic Fairness in Customer-oriented Applications: A Systematic Literature Review.
Automated decision-making systems are massively engaged in different types of
businesses, including customer-oriented sectors, and bring countless achievements in …
businesses, including customer-oriented sectors, and bring countless achievements in …
Deceptive fairness attacks on graphs via meta learning
We study deceptive fairness attacks on graphs to answer the following question: How can
we achieve poisoning attacks on a graph learning model to exacerbate the bias …
we achieve poisoning attacks on a graph learning model to exacerbate the bias …
Distributive justice as the foundational premise of fair ML: Unification, extension, and interpretation of group fairness metrics
Group fairness metrics are an established way of assessing the fairness of prediction-based
decision-making systems. However, these metrics are still insufficiently linked to …
decision-making systems. However, these metrics are still insufficiently linked to …
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
This paper introduces the Fair Fairness Benchmark (\textsf {FFB}), a benchmarking
framework for in-processing group fairness methods. Ensuring fairness in machine learning …
framework for in-processing group fairness methods. Ensuring fairness in machine learning …
Group fairness in prediction-based decision making: From moral assessment to implementation
Ensuring fairness of prediction-based decision making is based on statistical group fairness
criteria. Which one of these criteria is the morally most appropriate one depends on the …
criteria. Which one of these criteria is the morally most appropriate one depends on the …