A Justice-Based Framework for the Analysis of Algorithmic
Fairness-Utility Trade-Offs
- URL: http://arxiv.org/abs/2206.02891v3
- Date: Mon, 1 May 2023 21:37:09 GMT
- Title: A Justice-Based Framework for the Analysis of Algorithmic
Fairness-Utility Trade-Offs
- Authors: Corinna Hertweck, Joachim Baumann, Michele Loi, Eleonora Vigan\`o,
Christoph Heitz
- Abstract summary: In prediction-based decision-making systems, different perspectives can be at odds.
The short-term business goals of the decision makers are often in conflict with the decision subjects' wish to be treated fairly.
We propose a framework to make these value-laden choices clearly visible.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In prediction-based decision-making systems, different perspectives can be at
odds: The short-term business goals of the decision makers are often in
conflict with the decision subjects' wish to be treated fairly. Balancing these
two perspectives is a question of values. However, these values are often
hidden in the technicalities of the implementation of the decision-making
system. In this paper, we propose a framework to make these value-laden choices
clearly visible. We focus on a setting in which we want to find decision rules
that balance the perspective of the decision maker and of the decision
subjects. We provide an approach to formalize both perspectives, i.e., to
assess the utility of the decision maker and the fairness towards the decision
subjects. In both cases, the idea is to elicit values from decision makers and
decision subjects that are then turned into something measurable. For the
fairness evaluation, we build on well-known theories of distributive justice
and on the algorithmic literature to ask what a fair distribution of utility
(or welfare) looks like. This allows us to derive a fairness score that we then
compare to the decision maker's utility. As we focus on a setting in which we
are given a trained model and have to choose a decision rule, we use the
concept of Pareto efficiency to compare decision rules. Our proposed framework
can both guide the implementation of a decision-making system and help with
audits, as it allows us to resurface the values implemented in a
decision-making system.
Related papers
- (Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers [0.0]
Fairness metrics are used to assess discrimination and bias in decision-making processes across various domains.
We quantify the uncertainty of the disparity to enhance discrimination assessments.
We define preferences over decision-makers and utilize brute-force to choose the optimal decision-maker.
arXiv Detail & Related papers (2024-09-19T11:44:03Z) - Online Decision Mediation [72.80902932543474]
Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior.
In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
arXiv Detail & Related papers (2023-10-28T05:59:43Z) - 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) - On solving decision and risk management problems subject to uncertainty [91.3755431537592]
Uncertainty is a pervasive challenge in decision and risk management.
This paper develops a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them.
arXiv Detail & Related papers (2023-01-18T19:16:23Z) - 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) - Inverse Online Learning: Understanding Non-Stationary and Reactionary
Policies [79.60322329952453]
We show how to develop interpretable representations of how agents make decisions.
By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem.
We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them.
Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
arXiv Detail & Related papers (2022-03-14T17:40:42Z) - Explainable Decision Making with Lean and Argumentative Explanations [11.644036228274176]
We consider two variants of decision making, where "good" decisions amount to alternatives meeting "most" goals, and (ii) meeting "most preferred" goals.
We then define, for each variant and notion of "goodness," explanations in two formats, for justifying the selection of an alternative to audiences with differing needs and competences.
arXiv Detail & Related papers (2022-01-18T01:29:02Z) - Legal perspective on possible fairness measures - A legal discussion
using the example of hiring decisions (preprint) [0.0]
We explain the different kinds of fairness concepts that might be applicable for the specific application of hiring decisions.
We analyze their pros and cons with regard to the respective fairness interpretation and evaluate them from a legal perspective.
arXiv Detail & Related papers (2021-08-16T06:41:39Z) - Conceptualising Contestability: Perspectives on Contesting Algorithmic
Decisions [18.155121103400333]
We describe and analyse the perspectives of people and organisations who made submissions in response to Australia's proposed AI Ethics Framework'
Our findings reveal that while the nature of contestability is disputed, it is seen as a way to protect individuals, and it resembles contestability in relation to human decision-making.
arXiv Detail & Related papers (2021-02-23T05:13:18Z) - Predicting Court Decisions for Alimony: Avoiding Extra-legal Factors in
Decision made by Judges and Not Understandable AI Models [0.02578242050187029]
We present an explainable AI model designed in this purpose by combining a classification with random forest and a regression model.
By using a large amount of court decisions in matters of divorce produced by French jurisdictions, we seek to identify if there may be extra-legal factors in the decisions taken by the judges.
arXiv Detail & Related papers (2020-07-09T14:14:20Z) - Inverse Active Sensing: Modeling and Understanding Timely
Decision-Making [111.07204912245841]
We develop a framework for the general setting of evidence-based decision-making under endogenous, context-dependent time pressure.
We demonstrate how it enables modeling intuitive notions of surprise, suspense, and optimality in decision strategies.
arXiv Detail & Related papers (2020-06-25T02:30:45Z)
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.