The Legal Duty to Search for Less Discriminatory Algorithms
- URL: http://arxiv.org/abs/2406.06817v1
- Date: Mon, 10 Jun 2024 21:56:38 GMT
- Title: The Legal Duty to Search for Less Discriminatory Algorithms
- Authors: Emily Black, Logan Koepke, Pauline Kim, Solon Barocas, Mingwei Hsu,
- Abstract summary: We argue that the law should place a duty of a reasonable search for LDAs.
Model multiplicity and the availability of LDAs have significant ramifications for the legal response to discriminatory algorithms.
We argue that the law should place a duty of a reasonable search for LDAs on entities that develop and deploy predictive models in covered civil rights domains.
- Score: 4.625678906362822
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Work in computer science has established that, contrary to conventional wisdom, for a given prediction problem there are almost always multiple possible models with equivalent performance--a phenomenon often termed model multiplicity. Critically, different models of equivalent performance can produce different predictions for the same individual, and, in aggregate, exhibit different levels of impacts across demographic groups. Thus, when an algorithmic system displays a disparate impact, model multiplicity suggests that developers could discover an alternative model that performs equally well, but has less discriminatory impact. Indeed, the promise of model multiplicity is that an equally accurate, but less discriminatory algorithm (LDA) almost always exists. But without dedicated exploration, it is unlikely developers will discover potential LDAs. Model multiplicity and the availability of LDAs have significant ramifications for the legal response to discriminatory algorithms, in particular for disparate impact doctrine, which has long taken into account the availability of alternatives with less disparate effect when assessing liability. A close reading of legal authorities over the decades reveals that the law has on numerous occasions recognized that the existence of a less discriminatory alternative is sometimes relevant to a defendant's burden of justification at the second step of disparate impact analysis. Indeed, under disparate impact doctrine, it makes little sense to say that a given algorithmic system used by an employer, creditor, or housing provider is "necessary" if an equally accurate model that exhibits less disparate effect is available and possible to discover with reasonable effort. As a result, we argue that the law should place a duty of a reasonable search for LDAs on entities that develop and deploy predictive models in covered civil rights domains.
Related papers
- Fundamental Limits in the Search for Less Discriminatory Algorithms -- and How to Avoid Them [1.1411550157547312]
Disparate impact doctrine offers an important legal apparatus for targeting unfair data-driven algorithmic decisions.
This paper puts forward four fundamental results, which each represent limits to searching for and using less discriminatory algorithms (LDAs)
For each of our negative results limiting what is attainable in this setting, we offer positive results demonstrating that there exist effective and low-cost strategies.
arXiv Detail & Related papers (2024-12-24T03:49:48Z) - 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) - Fairness in Algorithmic Recourse Through the Lens of Substantive
Equality of Opportunity [15.78130132380848]
Algorithmic recourse has gained attention as a means of giving persons agency in their interactions with AI systems.
Recent work has shown that recourse itself may be unfair due to differences in the initial circumstances of individuals.
Time is a critical element in recourse because the longer it takes an individual to act, the more the setting may change.
arXiv Detail & Related papers (2024-01-29T11:55:45Z) - Endogenous Macrodynamics in Algorithmic Recourse [52.87956177581998]
Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment.
We show that many of the existing methodologies can be collectively described by a generalized framework.
We then argue that the existing framework does not account for a hidden external cost of recourse, that only reveals itself when studying the endogenous dynamics of recourse at the group level.
arXiv Detail & Related papers (2023-08-16T07:36:58Z) - Developing a Philosophical Framework for Fair Machine Learning: Lessons
From The Case of Algorithmic Collusion [0.0]
As machine learning algorithms are applied in new contexts the harms and injustices that result are qualitatively different.
The existing research paradigm in machine learning which develops metrics and definitions of fairness cannot account for these qualitatively different types of injustice.
I propose an ethical framework for researchers and practitioners in machine learning seeking to develop and apply fairness metrics.
arXiv Detail & Related papers (2022-07-05T16:21:56Z) - Few-shot Forgery Detection via Guided Adversarial Interpolation [56.59499187594308]
Existing forgery detection methods suffer from significant performance drops when applied to unseen novel forgery approaches.
We propose Guided Adversarial Interpolation (GAI) to overcome the few-shot forgery detection problem.
Our method is validated to be robust to choices of majority and minority forgery approaches.
arXiv Detail & Related papers (2022-04-12T16:05:10Z) - Reusing the Task-specific Classifier as a Discriminator:
Discriminator-free Adversarial Domain Adaptation [55.27563366506407]
We introduce a discriminator-free adversarial learning network (DALN) for unsupervised domain adaptation (UDA)
DALN achieves explicit domain alignment and category distinguishment through a unified objective.
DALN compares favorably against the existing state-of-the-art (SOTA) methods on a variety of public datasets.
arXiv Detail & Related papers (2022-04-08T04:40:18Z) - Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity [10.144058870887061]
We argue that individuals can be harmed when one predictor is chosen ad hoc from a group of equally well performing models.
Our findings suggest that such unfairness can be readily found in real life and it may be difficult to mitigate by technical means alone.
arXiv Detail & Related papers (2022-03-14T14:33:39Z) - Statistical discrimination in learning agents [64.78141757063142]
Statistical discrimination emerges in agent policies as a function of both the bias in the training population and of agent architecture.
We show that less discrimination emerges with agents that use recurrent neural networks, and when their training environment has less bias.
arXiv Detail & Related papers (2021-10-21T18:28:57Z) - A Low Rank Promoting Prior for Unsupervised Contrastive Learning [108.91406719395417]
We construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning.
Our hypothesis explicitly requires that all the samples belonging to the same instance class lie on the same subspace with small dimension.
Empirical evidences show that the proposed algorithm clearly surpasses the state-of-the-art approaches on multiple benchmarks.
arXiv Detail & Related papers (2021-08-05T15:58:25Z) - 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)
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.