Causal reinforcement learning: A survey
Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …
under uncertainty. Despite many remarkable achievements in recent decades, applying …
Fairsna: Algorithmic fairness in social network analysis
In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …
domains, including machine learning, natural language processing, and information …
Socially fair reinforcement learning
We consider the problem of episodic reinforcement learning where there are multiple
stakeholders with different reward functions. Our goal is to output a policy that is socially fair …
stakeholders with different reward functions. Our goal is to output a policy that is socially fair …
Policy advice and best practices on bias and fairness in AI
JM Alvarez, AB Colmenarejo, A Elobaid… - Ethics and Information …, 2024 - Springer
The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
Artificial Intelligence (AI) models are now being utilized in all facets of our lives such as
healthcare, education and employment. Since they are used in numerous sensitive …
healthcare, education and employment. Since they are used in numerous sensitive …
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
The widespread integration of Machine Learning systems in daily life, particularly in high-
stakes domains, has raised concerns about the fairness implications. While prior works have …
stakes domains, has raised concerns about the fairness implications. While prior works have …
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning
Fairness plays a crucial role in various multi-agent systems (eg, communication networks,
financial markets, etc.). Many multi-agent dynamical interactions can be cast as Markov …
financial markets, etc.). Many multi-agent dynamical interactions can be cast as Markov …
Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking
In collaborative human-robot order picking systems, human pickers and Autonomous Mobile
Robots (AMRs) travel independently through a warehouse and meet at pick locations where …
Robots (AMRs) travel independently through a warehouse and meet at pick locations where …
[HTML][HTML] FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering
Active Learning (AL) techniques have proven to be highly effective in reducing data labeling
costs across a range of machine learning tasks. Nevertheless, one known challenge of …
costs across a range of machine learning tasks. Nevertheless, one known challenge of …