Privacy and fairness in Federated learning: on the perspective of Tradeoff
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
Differential privacy and fairness in decisions and learning tasks: A survey
This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It
reviews the conditions under which privacy and fairness may have aligned or contrasting …
reviews the conditions under which privacy and fairness may have aligned or contrasting …
A comprehensive survey on automatic knowledge graph construction
Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …
knowledge. To this end, much effort has historically been spent extracting informative fact …
Differentially private empirical risk minimization under the fairness lens
Differential Privacy (DP) is an important privacy-enhancing technology for private machine
learning systems. It allows to measure and bound the risk associated with an individual …
learning systems. It allows to measure and bound the risk associated with an individual …
Loss balancing for fair supervised learning
MM Khalili, X Zhang… - … Conference on Machine …, 2023 - proceedings.mlr.press
Supervised learning models have been used in various domains such as lending, college
admission, face recognition, natural language processing, etc. However, they may inherit …
admission, face recognition, natural language processing, etc. However, they may inherit …
Individual fairness for local private graph neural network
Graph neural networks (GNNs) have shown superior performance in learning node
representation for various graph inference tasks and play a pivotal role in high-stakes …
representation for various graph inference tasks and play a pivotal role in high-stakes …
Fair sequential selection using supervised learning models
MM Khalili, X Zhang… - Advances in Neural …, 2021 - proceedings.neurips.cc
We consider a selection problem where sequentially arrived applicants apply for a limited
number of positions/jobs. At each time step, a decision maker accepts or rejects the given …
number of positions/jobs. At each time step, a decision maker accepts or rejects the given …
Holistic Survey of Privacy and Fairness in Machine Learning
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and
trustworthy Machine Learning (ML). Each objective has been independently studied in the …
trustworthy Machine Learning (ML). Each objective has been independently studied in the …
Survey on ai sustainability: Emerging trends on learning algorithms and research challenges
Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which
is attracting increasing attention because it promises to bring vast benefits for consumers …
is attracting increasing attention because it promises to bring vast benefits for consumers …
Fairness interventions as (dis) incentives for strategic manipulation
Although machine learning (ML) algorithms are widely used to make decisions about
individuals in various domains, concerns have arisen that (1) these algorithms are …
individuals in various domains, concerns have arisen that (1) these algorithms are …