… , the MMDM algorithm, and group fairness; in Section 3 we present out approach for fair
private federated learning; in Section 4 we describe our experimental results; and in Section 5 …

… privacy, particularly in connection with machine learning algorithms and Big Data. If privacy
still … issues of discrimination, equal opportunity, fairness and, more broadly, models of justice, …

… With the rapid increase in the use and deployment of machine learning (ML) systems in the
… that embody desirable qualities such as fairness, transparency, privacy, and robustness. In …

… to evaluate the fairness of machine learning models as well … that are core to debates about
fairness, including a review of … of machine learning to do quantitative work on fairness while …

… to fairness. Here we report first steps in modeling fairness in QIF as a dual concept to privacy…
A snapshot of the frontiers of fairness in machine learning. Communications of the ACM, 63(…

… fairness in load sharing in blockchain-based privacy-preserving learning, which is different
from the collaborative fairness … differential privacy for privacy-preserving machine learning on …

… ing fairness constraints in machine learning models. Our architecture consists of two adversaries:
(i) an adversarial reader that evaluates fairness … Neither private nor fair: Impact of data …

… Frequencies were determined using Orange.si., an open-source machine learning and data
… , fairness, and privacy reported in each article. The spreadsheet facilitated machine learning …

… We will further explore other privacy mechanisms in the future. However, it is nontrivial to
build fair machine learning models with mostly private sensitive attributes. First, private …

… Binns [36] introduces new notions of fairness that challenge both the common concept of
social welfare maximization and fair machine learning definitions, by asking questions such as: …