‘Equality of Opportunity for all Children’

Equality of opportunity requires that individuals should be selected for positions of advantage on the basis of relevant qualifications and that the ability to acquire these qualifications should not depend on the economic status of a person’s family. This chapter offers an institutional account of the moral basis of the first of these requirements. This account presupposes that positions of advantage are justified by the benefits they produce when they are held by individuals with the relevant abilities. The notion of ability relevant to considerations of procedural fairness therefore depends on the aims that justify the institution in question and on the way it is organized to promote these aims. The chapter relates this idea of fairness to the ideas of equal concern and non-discrimination and discusses the implications of procedural fairness for affirmative action.

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Psychological Stability

10.1093/oso/9780198825142.003.0008 ◽

2018 ◽

Author(s):

Robert Sugden

Keyword(s):

Market Economy ◽

Equality Of Opportunity ◽

Mutual Benefit ◽

General Belief ◽

Over Time

Chapter 8 asks what properties a market economy must have if it is to be psychologically stable—that is, if it is to reproduce a general belief that its governing principles are fair. I argue that, because of the division of knowledge and because the opportunities open to each person depend on how other people choose to use their opportunities, full equality of opportunity is not compatible with a market economy. Psychological stability has to rest on continuing expectations of mutual benefit, defined relative to a baseline that evolves over time and that cannot be justified in terms of abstract principles of fairness. However, if the market is to be recommended to each individual separately, each individual must be able to expect to share in the benefits that markets create. Maintaining such expectations typically requires redistributive mechanisms.

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Ethical Adversaries

ACM SIGKDD Explorations Newsletter ◽

10.1145/3468507.3468513 ◽

2021 ◽

Vol 23 (1) ◽

pp. 32-41

Author(s):

Pieter Delobelle ◽

Paul Temple ◽

Gilles Perrouin ◽

Benoit Frénay ◽

Patrick Heymans ◽

...

Keyword(s):

Machine Learning ◽

Equality Of Opportunity ◽

Decision Processes ◽

Critical Systems ◽

Trade Offs ◽

Theoretical Considerations

Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations. Individuals, as well as organisations, notice, test, and criticize unfair results to hold model designers and deployers accountable. We offer a framework that assists these groups in mitigating unfair representations stemming from the training datasets. Our framework relies on two inter-operating adversaries to improve fairness. First, a model is trained with the goal of preventing the guessing of protected attributes' values while limiting utility losses. This first step optimizes the model's parameters for fairness. Second, the framework leverages evasion attacks from adversarial machine learning to generate new examples that will be misclassified. These new examples are then used to retrain and improve the model in the first step. These two steps are iteratively applied until a significant improvement in fairness is obtained. We evaluated our framework on well-studied datasets in the fairness literature - including COMPAS - where it can surpass other approaches concerning demographic parity, equality of opportunity and also the model's utility. We investigated the trade-offs between these targets in terms of model hyperparameters and also illustrated our findings on the subtle difficulties when mitigating unfairness and highlight how our framework can assist model designers.

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