… There were several recent studies that incorporated both privacy and fairness considerations
to ML algorithms. Most of these studies have investigated the case of a centralized setting, …

… fairness constraints have become a significant issue for researchers and engineers. Machine
learning … and engineered correctly, that is, with considerations to fairness. [119] has a list of …

… Its origins are in legal fairness considerations for selection procedures which sometimes
use an 80% rule to deine if a process has disparate impact (ratio smaller than 0.8) or not [98]. …

… ML fairness is a recently established area of machine learning … Lack of consideration for the
specific patterns of unfairness … to the use of inappropriate fairness criteria in the design and …

… Achieving fairness necessitates not only mitigating bias in … the ethical considerations
surrounding bias and fairness in AI, … for mitigating bias and promoting fairness in AI systems, …

… 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 …

… formal rules and regulations that explicitly address issues of fairness in ML for healthcare
[92]. In this paper, we aim to contextualize fairness considerations within ML for clinical trials, to …

… machine learning to make decisions about people has increased, so has the drive to make
fairness-aware machine learning … As pointed out by Lehr and Ohm [24], such considerations …

… In both cases, explicit fairness considerations are rarely taken into account. The only exception
is, of course, affirmative action, which is imposed as an additional external constraint on …

… In other words, narrowly tailored considerations of fairness that operate within a broader
unfair context can perpetuate the harm— one group of people being imprisoned …