Learning fair representations via rebalancing graph structure
Abstract Graph Neural Network (GNN) models have been extensively researched and
utilised for extracting valuable insights from graph data. The performance of fairness …
utilised for extracting valuable insights from graph data. The performance of fairness …
Fairness and bias in algorithmic hiring
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
Algorithmic fairness is especially applicable in this domain due to its high stakes and …
Algorithmic fairness is especially applicable in this domain due to its high stakes and …
Auditing fairness under unawareness through counterfactual reasoning
Artificial intelligence (AI) is rapidly becoming the pivotal solution to support critical judgments
in many life-changing decisions. In fact, a biased AI tool can be particularly harmful since …
in many life-changing decisions. In fact, a biased AI tool can be particularly harmful since …
Measuring fairness of rankings under noisy sensitive information
Metrics commonly used to assess group fairness in ranking require the knowledge of group
membership labels (eg, whether a job applicant is male or female). Obtaining accurate …
membership labels (eg, whether a job applicant is male or female). Obtaining accurate …
[图书][B] Learning to quantify
This open access book provides an introduction and an overview of learning to quantify (aka
“quantification”), ie the task of training estimators of class proportions in unlabeled data by …
“quantification”), ie the task of training estimators of class proportions in unlabeled data by …
Interpretability in machine learning: on the interplay with explainability, predictive performances and models
Interpretability has recently gained attention in the field of machine learning, for it is crucial
when it comes to high-stakes decisions or troubleshooting. This abstract concept is hard to …
when it comes to high-stakes decisions or troubleshooting. This abstract concept is hard to …
Lazy data practices harm fairness research
Data practices shape research and practice on fairness in machine learning (fair ML).
Critical data studies offer important reflections and critiques for the responsible …
Critical data studies offer important reflections and critiques for the responsible …
Group-blind optimal transport to group parity and its constrained variants
Fairness holds a pivotal role in the realm of machine learning, particularly when it comes to
addressing groups categorised by sensitive attributes, eg, gender, race. Prevailing …
addressing groups categorised by sensitive attributes, eg, gender, race. Prevailing …
[PDF][PDF] Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
F ALESSANDRO, N BARANOWSKA… - arXiv preprint arXiv …, 2023 - graus.nu
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
Algorithmic fairness is especially applicable in this domain due to its high stakes and …
Algorithmic fairness is especially applicable in this domain due to its high stakes and …
What fairness metrics can really tell you: A case study in the educational domain
L Cohausz, J Kappenberger… - Proceedings of the 14th …, 2024 - dl.acm.org
Recently, discussions on fairness and algorithmic bias have gained prominence in the
learning analytics and educational data mining communities. To quantify algorithmic bias …
learning analytics and educational data mining communities. To quantify algorithmic bias …