Counterfactual Fairness Is Basically Demographic Parity
- URL: http://arxiv.org/abs/2208.03843v3
- Date: Sat, 11 Feb 2023 23:19:50 GMT
- Title: Counterfactual Fairness Is Basically Demographic Parity
- Authors: Lucas Rosenblatt and R. Teal Witter
- Abstract summary: Making fair decisions is crucial to ethically implementing machine learning algorithms in social settings.
We show that an algorithm which satisfies counterfactual fairness also satisfies demographic parity.
We formalize a concrete fairness goal: to preserve the order of individuals within protected groups.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Making fair decisions is crucial to ethically implementing machine learning
algorithms in social settings. In this work, we consider the celebrated
definition of counterfactual fairness [Kusner et al., NeurIPS, 2017]. We begin
by showing that an algorithm which satisfies counterfactual fairness also
satisfies demographic parity, a far simpler fairness constraint. Similarly, we
show that all algorithms satisfying demographic parity can be trivially
modified to satisfy counterfactual fairness. Together, our results indicate
that counterfactual fairness is basically equivalent to demographic parity,
which has important implications for the growing body of work on counterfactual
fairness. We then validate our theoretical findings empirically, analyzing
three existing algorithms for counterfactual fairness against three simple
benchmarks. We find that two simple benchmark algorithms outperform all three
existing algorithms -- in terms of fairness, accuracy, and efficiency -- on
several data sets. Our analysis leads us to formalize a concrete fairness goal:
to preserve the order of individuals within protected groups. We believe
transparency around the ordering of individuals within protected groups makes
fair algorithms more trustworthy. By design, the two simple benchmark
algorithms satisfy this goal while the existing algorithms for counterfactual
fairness do not.
Related papers
- Implementing Fairness: the view from a FairDream [0.0]
We train an AI model and develop our own fairness package FairDream to detect inequalities and then to correct for them.
Our experiments show that it is a property of FairDream to fulfill fairness objectives which are conditional on the ground truth.
arXiv Detail & Related papers (2024-07-20T06:06:24Z) - What's Distributive Justice Got to Do with It? Rethinking Algorithmic Fairness from the Perspective of Approximate Justice [1.8434042562191815]
We argue that in the context of imperfect decision-making systems, we should not only care about what the ideal distribution of benefits/harms among individuals would look like.
This requires us to rethink the way in which we, as algorithmic fairness researchers, view distributive justice and use fairness criteria.
arXiv Detail & Related papers (2024-07-17T11:13:23Z) - Causal Context Connects Counterfactual Fairness to Robust Prediction and
Group Fairness [15.83823345486604]
We motivatefactual fairness by showing that there is not a fundamental trade-off between fairness and accuracy.
Counterfactual fairness can sometimes be tested by measuring relatively simple group fairness metrics.
arXiv Detail & Related papers (2023-10-30T16:07:57Z) - FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods [84.1077756698332]
This paper introduces the Fair Fairness Benchmark (textsfFFB), a benchmarking framework for in-processing group fairness methods.
We provide a comprehensive analysis of state-of-the-art methods to ensure different notions of group fairness.
arXiv Detail & Related papers (2023-06-15T19:51:28Z) - DualFair: Fair Representation Learning at Both Group and Individual
Levels via Contrastive Self-supervision [73.80009454050858]
This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations.
Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
arXiv Detail & Related papers (2023-03-15T07:13:54Z) - How Robust is Your Fairness? Evaluating and Sustaining Fairness under
Unseen Distribution Shifts [107.72786199113183]
We propose a novel fairness learning method termed CUrvature MAtching (CUMA)
CUMA achieves robust fairness generalizable to unseen domains with unknown distributional shifts.
We evaluate our method on three popular fairness datasets.
arXiv Detail & Related papers (2022-07-04T02:37:50Z) - Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learning [14.428360876120333]
We argue that fairness measures are particularly sensitive to Goodhart's law.
We present a framework for moral reasoning about the justification of fairness metrics.
arXiv Detail & Related papers (2022-02-17T09:26:39Z) - Learning Fair Node Representations with Graph Counterfactual Fairness [56.32231787113689]
We propose graph counterfactual fairness, which considers the biases led by the above facts.
We generate counterfactuals corresponding to perturbations on each node's and their neighbors' sensitive attributes.
Our framework outperforms the state-of-the-art baselines in graph counterfactual fairness.
arXiv Detail & Related papers (2022-01-10T21:43:44Z) - MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
We study multi-group fairness in machine learning (MultiFair)
We propose a generic end-to-end algorithmic framework to solve it.
Our proposed framework is generalizable to many different settings.
arXiv Detail & Related papers (2021-05-24T02:30:22Z) - Metric-Free Individual Fairness with Cooperative Contextual Bandits [17.985752744098267]
Group fairness requires that different groups should be treated similarly which might be unfair to some individuals within a group.
Individual fairness remains understudied due to its reliance on problem-specific similarity metrics.
We propose a metric-free individual fairness and a cooperative contextual bandits algorithm.
arXiv Detail & Related papers (2020-11-13T03:10:35Z) - Two Simple Ways to Learn Individual Fairness Metrics from Data [47.6390279192406]
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness.
The lack of a widely accepted fair metric for many ML tasks is the main barrier to broader adoption of individual fairness.
We show empirically that fair training with the learned metrics leads to improved fairness on three machine learning tasks susceptible to gender and racial biases.
arXiv Detail & Related papers (2020-06-19T23:47:15Z)
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.