181 research outputs found
Recent Contributions to Theories of Discrimination
This paper surveys the literature on theories of discrimination, focusing
mainly on new contributions. Recent theories expand on the traditional
taste-based and statistical discrimination frameworks by considering specific
features of learning and signaling environments, often using novel information-
and mechanism-design language; analyzing learning and decision making by
algorithms; and introducing agents with behavioral biases and misspecified
beliefs. This survey also attempts to narrow the gap between the economic
perspective on ``theories of discrimination'' and the broader study of
discrimination in the social science literature. In that respect, I first
contribute by identifying a class of models of discriminatory institutions,
made up of theories of discriminatory social norms and discriminatory
institutional design. Second, I discuss the classification of discrimination as
direct or systemic, and compare it to other notions of discrimination in the
economic literature
Algorithmic Fairness in Business Analytics: Directions for Research and Practice
The extensive adoption of business analytics (BA) has brought financial gains
and increased efficiencies. However, these advances have simultaneously drawn
attention to rising legal and ethical challenges when BA inform decisions with
fairness implications. As a response to these concerns, the emerging study of
algorithmic fairness deals with algorithmic outputs that may result in
disparate outcomes or other forms of injustices for subgroups of the
population, especially those who have been historically marginalized. Fairness
is relevant on the basis of legal compliance, social responsibility, and
utility; if not adequately and systematically addressed, unfair BA systems may
lead to societal harms and may also threaten an organization's own survival,
its competitiveness, and overall performance. This paper offers a
forward-looking, BA-focused review of algorithmic fairness. We first review the
state-of-the-art research on sources and measures of bias, as well as bias
mitigation algorithms. We then provide a detailed discussion of the
utility-fairness relationship, emphasizing that the frequent assumption of a
trade-off between these two constructs is often mistaken or short-sighted.
Finally, we chart a path forward by identifying opportunities for business
scholars to address impactful, open challenges that are key to the effective
and responsible deployment of BA
Long-Term Fairness with Unknown Dynamics
While machine learning can myopically reinforce social inequalities, it may
also be used to dynamically seek equitable outcomes. In this paper, we
formalize long-term fairness in the context of online reinforcement learning.
This formulation can accommodate dynamical control objectives, such as driving
equity inherent in the state of a population, that cannot be incorporated into
static formulations of fairness. We demonstrate that this framing allows an
algorithm to adapt to unknown dynamics by sacrificing short-term incentives to
drive a classifier-population system towards more desirable equilibria. For the
proposed setting, we develop an algorithm that adapts recent work in online
learning. We prove that this algorithm achieves simultaneous probabilistic
bounds on cumulative loss and cumulative violations of fairness (as statistical
regularities between demographic groups). We compare our proposed algorithm to
the repeated retraining of myopic classifiers, as a baseline, and to a deep
reinforcement learning algorithm that lacks safety guarantees. Our experiments
model human populations according to evolutionary game theory and integrate
real-world datasets
What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective
Algorithmic fairness has attracted increasing attention in the machine
learning community. Various definitions are proposed in the literature, but the
differences and connections among them are not clearly addressed. In this
paper, we review and reflect on various fairness notions previously proposed in
machine learning literature, and make an attempt to draw connections to
arguments in moral and political philosophy, especially theories of justice. We
also consider fairness inquiries from a dynamic perspective, and further
consider the long-term impact that is induced by current prediction and
decision. In light of the differences in the characterized fairness, we present
a flowchart that encompasses implicit assumptions and expected outcomes of
different types of fairness inquiries on the data generating process, on the
predicted outcome, and on the induced impact, respectively. This paper
demonstrates the importance of matching the mission (which kind of fairness one
would like to enforce) and the means (which spectrum of fairness analysis is of
interest, what is the appropriate analyzing scheme) to fulfill the intended
purpose
Fair machine learning under partial compliance
Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance by k% of employers can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; (4) partial compliance based on local parity measures can induce extreme segregation. Finally, we discuss implications for auditors and insights concerning the design of regulatory frameworks
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