A comparative analysis of bias amplification in graph neural network approaches for recommender systems
Recommender Systems (RSs) are used to provide users with personalized item
recommendations and help them overcome the problem of information overload. Currently …
recommendations and help them overcome the problem of information overload. Currently …
Fair enough: Searching for sufficient measures of fairness
Testing machine learning software for ethical bias has become a pressing current concern.
In response, recent research has proposed a plethora of new fairness metrics, for example …
In response, recent research has proposed a plethora of new fairness metrics, for example …
Bias assessment approaches for addressing user-centered fairness in GNN-based recommender systems
In today's technology-driven society, many decisions are made based on the results
provided by machine learning algorithms. It is widely known that the models generated by …
provided by machine learning algorithms. It is widely known that the models generated by …
[PDF][PDF] Quantifying Fairness Disparities in Graph-Based Neural Network Recommender Systems for Protected Groups.
The wide acceptance of Recommender Systems (RS) among users for product and service
suggestions has led to the proposal of multiple recommendation methods that have …
suggestions has led to the proposal of multiple recommendation methods that have …
Fair and interpretable algorithmic hiring using evolutionary many objective optimization
M Geden, J Andrews - Proceedings of the AAAI Conference on Artificial …, 2021 - ojs.aaai.org
Hiring is a high-stakes decision-making process that balances the joint objectives of being
fair and accurately selecting the top candidates. The industry standard method employs …
fair and accurately selecting the top candidates. The industry standard method employs …
Intersectional Fairness in Machine Learning: Measurements, Algorithms, and Applications
R Islam - 2022 - search.proquest.com
With the increasing impact of machine learning (ML) algorithms on many facets of life, there
are growing concerns that biases inherent in data can lead the behavior of these algorithms …
are growing concerns that biases inherent in data can lead the behavior of these algorithms …