The Fairness Field Guide: Perspectives from Social and Formal Sciences

• URL: http://arxiv.org/abs/2201.05216v1
• Date: Thu, 13 Jan 2022 21:30:03 GMT
• Title: The Fairness Field Guide: Perspectives from Social and Formal Sciences
• Authors: Alycia N. Carey and Xintao Wu
• Abstract summary: There is a critical lack of literature that explains the interplay of fair machine learning with philosophy, sociology, and law. We give the mathematical and algorithmic backgrounds of several popular statistical and causal-based fair machine learning methods. We explore several criticisms of the current approaches to fair machine learning from sociological and philosophical viewpoints.
• Score: 16.53498469585148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the past several years, a slew of different methods to measure the fairness of a machine learning model have been proposed. However, despite the growing number of publications and implementations, there is still a critical lack of literature that explains the interplay of fair machine learning with the social sciences of philosophy, sociology, and law. We hope to remedy this issue by accumulating and expounding upon the thoughts and discussions of fair machine learning produced by both social and formal (specifically machine learning and statistics) sciences in this field guide. Specifically, in addition to giving the mathematical and algorithmic backgrounds of several popular statistical and causal-based fair machine learning methods, we explain the underlying philosophical and legal thoughts that support them. Further, we explore several criticisms of the current approaches to fair machine learning from sociological and philosophical viewpoints. It is our hope that this field guide will help fair machine learning practitioners better understand how their algorithms align with important humanistic values (such as fairness) and how we can, as a field, design methods and metrics to better serve oppressed and marginalized populaces.

Related papers

• A Catalog of Fairness-Aware Practices in Machine Learning Engineering [13.012624574172863]
  Machine learning's widespread adoption in decision-making processes raises concerns about fairness. There remains a gap in understanding and categorizing practices for engineering fairness throughout the machine learning lifecycle. This paper presents a novel catalog of practices for addressing fairness in machine learning derived from a systematic mapping study.
  arXiv  Detail & Related papers  (2024-08-29T16:28:43Z)
• A Benchmark for Fairness-Aware Graph Learning [58.515305543487386]
  We present an extensive benchmark on ten representative fairness-aware graph learning methods. Our in-depth analysis reveals key insights into the strengths and limitations of existing methods.
  arXiv  Detail & Related papers  (2024-07-16T18:43:43Z)
• A Survey of Deep Learning for Mathematical Reasoning [71.88150173381153]
  We review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. Recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning.
  arXiv  Detail & Related papers  (2022-12-20T18:46:16Z)
• A Systematic Approach to Group Fairness in Automated Decision Making [0.0]
  The goal of this paper is to provide data scientists with an accessible introduction to group fairness metrics. We will do this by considering in which sense socio-demographic groups are compared for making a statement on fairness.
  arXiv  Detail & Related papers  (2021-09-09T12:47:15Z)
• The zoo of Fairness metrics in Machine Learning [62.997667081978825]
  In recent years, the problem of addressing fairness in Machine Learning (ML) and automatic decision-making has attracted a lot of attention. A plethora of different definitions of fairness in ML have been proposed, that consider different notions of what is a "fair decision" in situations impacting individuals in the population. In this work, we try to make some order out of this zoo of definitions.
  arXiv  Detail & Related papers  (2021-06-01T13:19:30Z)
• Individual Explanations in Machine Learning Models: A Survey for Practitioners [69.02688684221265]
  The use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise. Many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways. Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models.
  arXiv  Detail & Related papers  (2021-04-09T01:46:34Z)
• The Use and Misuse of Counterfactuals in Ethical Machine Learning [2.28438857884398]
  We argue for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We conclude that the counterfactual approach in machine learning fairness and social explainability can require an incoherent theory of what social categories are.
  arXiv  Detail & Related papers  (2021-02-09T19:28:41Z)
• Fairness in Machine Learning [15.934879442202785]
  We show how causal Bayesian networks can play an important role to reason about and deal with fairness. We present a unified framework that encompasses methods that can deal with different settings and fairness criteria.
  arXiv  Detail & Related papers  (2020-12-31T18:38:58Z)
• Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning [69.99522650448213]
  Research in machine learning is at a turning point. Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks. This white paper provides an introduction and discussion of this emerging field in machine learning research.
  arXiv  Detail & Related papers  (2020-12-21T15:07:19Z)
• Fairness in Machine Learning: A Survey [0.0]
  There is significant literature on approaches to mitigate bias and promote fairness. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas.
  arXiv  Detail & Related papers  (2020-10-04T21:01:34Z)
• On Consequentialism and Fairness [64.35872952140677]
  We provide a consequentialist critique of common definitions of fairness within machine learning. We conclude with a broader discussion of the issues of learning and randomization.
  arXiv  Detail & Related papers  (2020-01-02T05:39:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.