Shortcomings of Counterfactual Fairness and a Proposed Modification
- URL: http://arxiv.org/abs/2011.07312v1
- Date: Sat, 14 Nov 2020 14:49:51 GMT
- Title: Shortcomings of Counterfactual Fairness and a Proposed Modification
- Authors: Fabian Beigang
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, I argue that counterfactual fairness does not constitute a
necessary condition for an algorithm to be fair, and subsequently suggest how
the constraint can be modified in order to remedy this shortcoming. To this
end, I discuss a hypothetical scenario in which counterfactual fairness and an
intuitive judgment of fairness come apart. Then, I turn to the question how the
concept of discrimination can be explicated in order to examine the
shortcomings of counterfactual fairness as a necessary condition of algorithmic
fairness in more detail. I then incorporate the insights of this analysis into
a novel fairness constraint, causal relevance fairness, which is a modification
of the counterfactual fairness constraint that seems to circumvent its
shortcomings.
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) - 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) - What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning [52.51430732904994]
In reinforcement learning problems, agents must consider long-term fairness while maximizing returns.
Recent works have proposed many different types of fairness notions, but how unfairness arises in RL problems remains unclear.
We introduce a novel notion called dynamics fairness, which explicitly captures the inequality stemming from environmental dynamics.
arXiv Detail & Related papers (2024-04-16T22:47:59Z) - Understanding Fairness Surrogate Functions in Algorithmic Fairness [21.555040357521907]
We show that there is a surrogate-fairness gap between the fairness definition and the fairness surrogate function.
We elaborate a novel and general algorithm called Balanced Surrogate, which iteratively reduces the gap to mitigate unfairness.
arXiv Detail & Related papers (2023-10-17T12:40:53Z) - Reconciling Predictive and Statistical Parity: A Causal Approach [68.59381759875734]
We propose a new causal decomposition formula for the fairness measures associated with predictive parity.
We show that the notions of statistical and predictive parity are not really mutually exclusive, but complementary and spanning a spectrum of fairness notions.
arXiv Detail & Related papers (2023-06-08T09:23:22Z) - 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) - SLIDE: a surrogate fairness constraint to ensure fairness consistency [1.3649494534428745]
We propose a new surrogate fairness constraint called SLIDE, which is feasible and achieves a fast convergence rate.
Numerical experiments confirm that SLIDE works well for various benchmark datasets.
arXiv Detail & Related papers (2022-02-07T13:50:21Z) - 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) - Impossibility of What? Formal and Substantive Equality in Algorithmic
Fairness [3.42658286826597]
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"
arXiv Detail & Related papers (2021-07-09T19:29:57Z) - Algorithmic Decision Making with Conditional Fairness [48.76267073341723]
We define conditional fairness as a more sound fairness metric by conditioning on the fairness variables.
We propose a Derivable Conditional Fairness Regularizer (DCFR) to track the trade-off between precision and fairness of algorithmic decision making.
arXiv Detail & Related papers (2020-06-18T12:56:28Z)
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.