The Long Arc of Fairness: Formalisations and Ethical Discourse
- URL: http://arxiv.org/abs/2203.06038v1
- Date: Tue, 8 Mar 2022 09:54:59 GMT
- Title: The Long Arc of Fairness: Formalisations and Ethical Discourse
- Authors: Pola Schw\"obel, Peter Remmers
- Abstract summary: We argue that the relations between technical (formalised) and ethical discourse on fairness are not always clear and productive.
We introduce dynamic fairness modelling, a more comprehensive approach that realigns formal fairness metrics with arguments from the ethical discourse.
By contextualising these elements of fairness-related processes, dynamic fairness modelling explicates formerly latent ethical aspects.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, the idea of formalising and modelling fairness for
algorithmic decision making (ADM) has advanced to a point of sophisticated
specialisation. However, the relations between technical (formalised) and
ethical discourse on fairness are not always clear and productive. Arguing for
an alternative perspective, we review existing fairness metrics and discuss
some common issues. For instance, the fairness of procedures and distributions
is often formalised and discussed statically, disregarding both structural
preconditions of the status quo and downstream effects of a given intervention.
We then introduce dynamic fairness modelling, a more comprehensive approach
that realigns formal fairness metrics with arguments from the ethical
discourse. A dynamic fairness model incorporates (1) ethical goals, (2) formal
metrics to quantify decision procedures and outcomes and (3) mid-term or
long-term downstream effects. By contextualising these elements of
fairness-related processes, dynamic fairness modelling explicates formerly
latent ethical aspects and thereby provides a helpful tool to navigate
trade-offs between different fairness interventions. To illustrate the
framework, we discuss an example application -- the current European efforts to
increase the number of women on company boards, e.g. via quota solutions -- and
present early technical work that fits within our framework.
Related papers
- Dispute resolution in legal mediation with quantitative argumentation [0.0]
We introduce a QuAM framework, which integrates the parties' knowledge and the mediator's knowledge, including facts and legal norms, when determining the acceptability of a mediation goal.
We also develop a new formalism to model the relationship between the acceptability of a goal argument and the values assigned to a variable associated with the argument.
arXiv Detail & Related papers (2024-09-25T12:05:46Z) - Sequential Representation Learning via Static-Dynamic Conditional Disentanglement [58.19137637859017]
This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-independent and time-varying factors in videos.
We propose a new model that breaks the usual independence assumption between those factors by explicitly accounting for the causal relationship between the static/dynamic variables.
Experiments show that the proposed approach outperforms previous complex state-of-the-art techniques in scenarios where the dynamics of a scene are influenced by its content.
arXiv Detail & Related papers (2024-08-10T17:04:39Z) - Fairness Explainability using Optimal Transport with Applications in
Image Classification [0.46040036610482665]
We propose a comprehensive approach to uncover the causes of discrimination in Machine Learning applications.
We leverage Wasserstein barycenters to achieve fair predictions and introduce an extension to pinpoint bias-associated regions.
This allows us to derive a cohesive system which uses the enforced fairness to measure each features influence emphon the bias.
arXiv Detail & Related papers (2023-08-22T00:10:23Z) - ACROCPoLis: A Descriptive Framework for Making Sense of Fairness [6.4686347616068005]
We propose the ACROCPoLis framework to represent allocation processes with a modeling emphasis on fairness aspects.
The framework provides a shared vocabulary in which the factors relevant to fairness assessments for different situations and procedures are made explicit.
arXiv Detail & Related papers (2023-04-19T21:14:57Z) - 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) - 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 Increases Adversarial Vulnerability [50.90773979394264]
This paper shows the existence of a dichotomy between fairness and robustness, and analyzes when achieving fairness decreases the model robustness to adversarial samples.
Experiments on non-linear models and different architectures validate the theoretical findings in multiple vision domains.
The paper proposes a simple, yet effective, solution to construct models achieving good tradeoffs between fairness and robustness.
arXiv Detail & Related papers (2022-11-21T19:55:35Z) - Causal Fairness Analysis [68.12191782657437]
We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings.
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms.
Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
arXiv Detail & Related papers (2022-07-23T01:06:34Z) - Exploring the Trade-off between Plausibility, Change Intensity and
Adversarial Power in Counterfactual Explanations using Multi-objective
Optimization [73.89239820192894]
We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances.
We present a novel framework for the generation of counterfactual examples.
arXiv Detail & Related papers (2022-05-20T15:02:53Z) - Promises and Challenges of Causality for Ethical Machine Learning [2.1946447418179664]
We lay out the conditions for appropriate application of causal fairness under the "potential outcomes framework"
We highlight key aspects of causal inference that are often ignored in the causal fairness literature.
We argue that such conceptualization of the intervention is key in evaluating the validity of causal assumptions.
arXiv Detail & Related papers (2022-01-26T00:04:10Z)
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.