Settings such as lending and policing can be modeled by a
centralized agent allocating a scarce resource (e.g. loans or
police officers) amongst several groups, in order to maximize
some objective (e.g. loans given that are repaid, or criminals
that are apprehended). Often in such problems fairness is also
a concern. One natural notion of fairness, based on general
principles of equality of opportunity, asks that conditional on
an individual being a candidate for the resource in question,
the probability of actually receiving it is approximately independent of the individual’s group. For example, in lending this
would mean that equally creditworthy individuals in different
racial groups have roughly equal chances of receiving a loan.
In policing it would mean that two individuals committing
the same crime in different districts would have roughly equal
chances of being arrested.
In this paper, we formalize this general notion of fairness
for allocation problems and investigate its algorithmic consequences. Our main technical results include an efficient learning algorithm that converges to an optimal fair allocation even
when the allocator does not know the frequency of candidates
(i.e. creditworthy individuals or criminals) in each group. This
algorithm operates in a censored feedback model in which
only the number of candidates who received the resource in a
given allocation can be observed, rather than the true number
of candidates in each group. This models the fact that we do
not learn the creditworthiness of individuals we do not give
loans to and do not learn about crimes committed if the police
presence in a district is low.
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Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines
In this work we use Equal Opportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness. We contrast formal EO approaches that narrowly focus on fair contests at discrete decision points, with substantive EO doctrines that look at people’s fair life chances more holistically over the course of a lifetime. We use this taxonomy to provide a moral interpretation of the impossibility results as the incompatibility between different conceptions of a fair contest — foward-facing versus backward-facing — when people do not have fair life chances. We use this result to motivate substantive conceptions of algorithmic fairness and outline two plausible fair decision procedures based on the luck egalitarian doctrine of EO, and Rawls’s principle of fair equality of opportunity.
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- NSF-PAR ID:
- 10398897
- Date Published:
- Journal Name:
- EAAMO '22: Equity and Access in Algorithms, Mechanisms, and Optimization
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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As algorithmic decision making is increasingly deployed in every walk of life, many researchers have raised concerns about fairness-related bias from such algorithms. But there is little research on harnessing psychometric methods to uncover potential discriminatory bias inside decision-making algorithms. The main goal of this article is to propose a new framework for algorithmic fairness based on differential item functioning (DIF), which has been commonly used to measure item fairness in psychometrics. Our fairness notion, which we call differential algorithmic functioning (DAF), is defined based on three pieces of information: a decision variable, a “fair” variable, and a protected variable such as race or gender. Under the DAF framework, an algorithm can exhibit uniform DAF, nonuniform DAF, or neither (i.e., non-DAF). For detecting DAF, we provide modifications of well-established DIF methods: Mantel–Haenszel test, logistic regression, and residual-based DIF. We demonstrate our framework through a real dataset concerning decision-making algorithms for grade retention in K–12 education in the United States.
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Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithm to be “fair.” Most of these propositions are inspired by, but inadequately grounded in, scholarship from political philosophy. This comic aims to correct that deficit. We begin by setting up a working definition of an 'Automated Decision System' (ADS) and explaining 'bias' in outputs of an ADS. We then critically evaluate different definitions of fairness as Equality of Opportunity (EOP) by contrasting their conception in political philosophy (such as Rawls’s fair EOP and formal EOP) with the proposed codification in Fair-ML (such as statistical parity, equality of odds and accuracy) to provide a clearer lens with which to view existing results and to identify future research directions. We use this framing to reinterpret the impossibility results as the incompatibility between different EOP doctrines and demonstrate how political philosophy can provide normative guidance as to which notion of fairness is applicable in which context. We conclude by highlighting justice considerations that the fair-ML literature currently overlooks or underemphasizes, such as Rawls's broader theory of justice, which supplements his EOP principle with a principle guaranteeing equal rights and liberties to all citizens in a free and democratic society.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3551624.3555303
