Discrimination in algorithmic decision making
I Valera - Fundamental Questions, 2021 - nomos-elibrary.de
As automated data analysis supplements, and even replaces, human supervision in
decision making, there are growing societal concerns about potential unfairness of these …
decision making, there are growing societal concerns about potential unfairness of these …
[HTML][HTML] Non-empirical problems in fair machine learning
T Scantamburlo - Ethics and Information Technology, 2021 - Springer
The problem of fair machine learning has drawn much attention over the last few years and
the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also …
the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also …
[PDF][PDF] Benchmarking four approaches to fairness-aware machine learning
E Hamilton - 2017 - scholarship.tricolib.brynmawr.edu
Machine learning has begun to automate decisions that profoundly affect individuals, raising
concerns about the technology's effectiveness, fairness, and the extent to which it obfuscates …
concerns about the technology's effectiveness, fairness, and the extent to which it obfuscates …
[图书][B] Fairness and machine learning: Limitations and opportunities
An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …
fairness and machine learning. Fairness and Machine Learning introduces advanced …
The measure and mismeasure of fairness
The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …
equitable. Over the last decade, several formal, mathematical definitions of fairness have …
Multi-stage bias mitigation for individual fairness in algorithmic decisions
The widespread use of machine learning algorithms in data-driven decision-making systems
has become increasingly popular. Recent studies have raised concerns that this increasing …
has become increasingly popular. Recent studies have raised concerns that this increasing …
Fair and unbiased algorithmic decision making: Current state and future challenges
S Tolan - arXiv preprint arXiv:1901.04730, 2019 - arxiv.org
Machine learning algorithms are now frequently used in sensitive contexts that substantially
affect the course of human lives, such as credit lending or criminal justice. This is driven by …
affect the course of human lives, such as credit lending or criminal justice. This is driven by …
The invisible power of fairness. how machine learning shapes democracy
Many machine learning systems make extensive use of large amounts of data regarding
human behaviors. Several researchers have found various discriminatory practices related …
human behaviors. Several researchers have found various discriminatory practices related …
Long-term impacts of fair machine learning
Machine learning models developed from real-world data can inherit potential, preexisting
bias in the dataset. When these models are used to inform decisions involving human …
bias in the dataset. When these models are used to inform decisions involving human …
Mathematical notions vs. human perception of fairness: A descriptive approach to fairness for machine learning
Fairness for Machine Learning has received considerable attention, recently. Various
mathematical formulations of fairness have been proposed, and it has been shown that it is …
mathematical formulations of fairness have been proposed, and it has been shown that it is …