Algorithmic Fairness Alfa Leiden Computational Network Science

Algorithmic Fairness Alfa Leiden Computational Network Science Algorithmic fairness (alfa) is a special interest group doing research on understanding biases and designing fair algorithms in network science and data science. The candidate will be involved in frontier research on developing fairness aware algorithms, as well as analyzing biases and inequalities in large scale social networks.

Vacancy Phd Position On Algorithmic Fairness In Social Network The candidate will work on analyzing fairness and biases in network based algorithms, develop fairness aware algorithms, and design intervention methods to promote fairness in networks. Saxena, a.; fletcher, g.; pechenizkiy, m. (2024) fairsna: algorithmic fairness in social network analysis article letter to editor all authors saxena, a.; fletcher, g.; pechenizkiy, m. date 2024 04 26 journal acm computing surveys volume 56 issue 8 doi doi:10.1145 3653711. We review the state of the art for diferent research topics in sna, including the considered fairness constraints, their limitations, and our vision. this survey also covers evaluation metrics, available datasets and synthetic network generating models used in such studies. The project aims to push the boundaries of current knowledge in network science, offering innovative solutions to pressing issues such as bias and fairness in algorithmic decision making.

Algorithmic Fairness Towards Data Science We review the state of the art for diferent research topics in sna, including the considered fairness constraints, their limitations, and our vision. this survey also covers evaluation metrics, available datasets and synthetic network generating models used in such studies. The project aims to push the boundaries of current knowledge in network science, offering innovative solutions to pressing issues such as bias and fairness in algorithmic decision making. The candidate will work on analyzing fairness and biases in network based algorithms, develop fairness aware algorithms, and design intervention methods to promote fairness in networks. This article analyzes the current status and challenges of algorithmic fairness from three key perspectives: fairness definition, fairness dataset, and fairness algorithm. furthermore, the potential directions and strategies to promote the fairness of the algorithm are proposed. His latest results address transparency in personalization, the role of human mobility in privacy across several domains, the efficiency of crowdsourced content curation, the fairness of incentives and algorithms used in social networking. The candidate will work on analyzing fairness and biases in network based algorithms, develop fairness aware algorithms, and design intervention methods to promote fairness in.

About Leiden Computational Network Science The candidate will work on analyzing fairness and biases in network based algorithms, develop fairness aware algorithms, and design intervention methods to promote fairness in networks. This article analyzes the current status and challenges of algorithmic fairness from three key perspectives: fairness definition, fairness dataset, and fairness algorithm. furthermore, the potential directions and strategies to promote the fairness of the algorithm are proposed. His latest results address transparency in personalization, the role of human mobility in privacy across several domains, the efficiency of crowdsourced content curation, the fairness of incentives and algorithms used in social networking. The candidate will work on analyzing fairness and biases in network based algorithms, develop fairness aware algorithms, and design intervention methods to promote fairness in.