Impossibility of What? Formal and Substantive Equality in Algorithmic
Fairness
- URL: http://arxiv.org/abs/2107.04642v1
- Date: Fri, 9 Jul 2021 19:29:57 GMT
- Title: Impossibility of What? Formal and Substantive Equality in Algorithmic
Fairness
- Authors: Ben Green
- Abstract summary: I argue that the dominant, "formal" approach to algorithmic fairness is ill-equipped as a framework for pursuing equality.
I propose an alternative: a "substantive" approach to algorithmic fairness that centers opposition to social hierarchies.
The distinction between formal and substantive algorithmic fairness is exemplified by each approach's responses to the "impossibility of fairness"
- Score: 3.42658286826597
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the face of compounding crises of social and economic inequality, many
have turned to algorithmic decision-making to achieve greater fairness in
society. As these efforts intensify, reasoning within the burgeoning field of
"algorithmic fairness" increasingly shapes how fairness manifests in practice.
This paper interrogates whether algorithmic fairness provides the appropriate
conceptual and practical tools for enhancing social equality. I argue that the
dominant, "formal" approach to algorithmic fairness is ill-equipped as a
framework for pursuing equality, as its narrow frame of analysis generates
restrictive approaches to reform. In light of these shortcomings, I propose an
alternative: a "substantive" approach to algorithmic fairness that centers
opposition to social hierarchies and provides a more expansive analysis of how
to address inequality. This substantive approach enables more fruitful
theorizing about the role of algorithms in combatting oppression. The
distinction between formal and substantive algorithmic fairness is exemplified
by each approach's responses to the "impossibility of fairness" (an
incompatibility between mathematical definitions of algorithmic fairness).
While the formal approach requires us to accept the "impossibility of fairness"
as a harsh limit on efforts to enhance equality, the substantive approach
allows us to escape the "impossibility of fairness" by suggesting reforms that
are not subject to this false dilemma and that are better equipped to
ameliorate conditions of social oppression.
Related papers
- 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) - Fairness-Accuracy Trade-Offs: A Causal Perspective [58.06306331390586]
We analyze the tension between fairness and accuracy from a causal lens for the first time.
We show that enforcing a causal constraint often reduces the disparity between demographic groups.
We introduce a new neural approach for causally-constrained fair learning.
arXiv Detail & Related papers (2024-05-24T11:19:52Z) - Causal Fairness for Outcome Control [68.12191782657437]
We study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable.
In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision.
We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this.
arXiv Detail & Related papers (2023-06-08T09:31:18Z) - Fair Enough: Standardizing Evaluation and Model Selection for Fairness
Research in NLP [64.45845091719002]
Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct.
This paper seeks to clarify the current situation and plot a course for meaningful progress in fair learning.
arXiv Detail & Related papers (2023-02-11T14:54:00Z) - Fairness in Matching under Uncertainty [78.39459690570531]
algorithmic two-sided marketplaces have drawn attention to the issue of fairness in such settings.
We axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits.
We design a linear programming framework to find fair utility-maximizing distributions over allocations.
arXiv Detail & Related papers (2023-02-08T00:30:32Z) - The Unfairness of Fair Machine Learning: Levelling down and strict
egalitarianism by default [10.281644134255576]
This paper examines the causes and prevalence of levelling down across fairML.
We propose a first step towards substantive equality in fairML by design through enforcement of minimum acceptable harm thresholds.
arXiv Detail & Related papers (2023-02-05T15:22:43Z) - It's Not Fairness, and It's Not Fair: The Failure of Distributional
Equality and the Promise of Relational Equality in Complete-Information
Hiring Games [2.8935588665357077]
existing discrimination and injustice is often the result of unequal social relations, rather than an unequal distribution of resources.
We show how optimizing for existing computational and economic definitions of fairness and equality fail to prevent unequal social relations.
arXiv Detail & Related papers (2022-09-12T20:35:42Z) - Domain Adaptation meets Individual Fairness. And they get along [48.95808607591299]
We show that algorithmic fairness interventions can help machine learning models overcome distribution shifts.
In particular, we show that enforcing suitable notions of individual fairness (IF) can improve the out-of-distribution accuracy of ML models.
arXiv Detail & Related papers (2022-05-01T16:19:55Z) - Optimal Algorithms for Decentralized Stochastic Variational Inequalities [113.43047601775453]
This work concentrates on the decentralized setting, which is increasingly important but not well understood.
We present lower bounds for both communication and local iterations and construct optimal algorithms that match these lower bounds.
Our algorithms are the best among the available not only in the decentralized case, but also in the deterministic and non-distributed literature.
arXiv Detail & Related papers (2022-02-06T13:14:02Z) - Affirmative Algorithms: The Legal Grounds for Fairness as Awareness [0.0]
We discuss how such approaches will likely be deemed "algorithmic affirmative action"
We argue that the government-contracting cases offer an alternative grounding for algorithmic fairness.
We call for more research at the intersection of algorithmic fairness and causal inference to ensure that bias mitigation is tailored to specific causes and mechanisms of bias.
arXiv Detail & Related papers (2020-12-18T22:53:20Z) - Shortcomings of Counterfactual Fairness and a Proposed Modification [0.0]
I argue that counterfactual fairness does not constitute a necessary condition for an algorithm to be fair.
I then suggest how the constraint can be modified in order to remedy this shortcoming.
arXiv Detail & Related papers (2020-11-14T14:49:51Z)
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.