2,406 research outputs found
Matching Code and Law: Achieving Algorithmic Fairness with Optimal Transport
Increasingly, discrimination by algorithms is perceived as a societal and
legal problem. As a response, a number of criteria for implementing algorithmic
fairness in machine learning have been developed in the literature. This paper
proposes the Continuous Fairness Algorithm (CFA) which enables a
continuous interpolation between different fairness definitions. More
specifically, we make three main contributions to the existing literature.
First, our approach allows the decision maker to continuously vary between
specific concepts of individual and group fairness. As a consequence, the
algorithm enables the decision maker to adopt intermediate ``worldviews'' on
the degree of discrimination encoded in algorithmic processes, adding nuance to
the extreme cases of ``we're all equal'' (WAE) and ``what you see is what you
get'' (WYSIWYG) proposed so far in the literature. Second, we use optimal
transport theory, and specifically the concept of the barycenter, to maximize
decision maker utility under the chosen fairness constraints. Third, the
algorithm is able to handle cases of intersectionality, i.e., of
multi-dimensional discrimination of certain groups on grounds of several
criteria. We discuss three main examples (credit applications; college
admissions; insurance contracts) and map out the legal and policy implications
of our approach. The explicit formalization of the trade-off between individual
and group fairness allows this post-processing approach to be tailored to
different situational contexts in which one or the other fairness criterion may
take precedence. Finally, we evaluate our model experimentally.Comment: Vastly extended new version, now including computational experiment
Multiwinner Voting with Fairness Constraints
Multiwinner voting rules are used to select a small representative subset of
candidates or items from a larger set given the preferences of voters. However,
if candidates have sensitive attributes such as gender or ethnicity (when
selecting a committee), or specified types such as political leaning (when
selecting a subset of news items), an algorithm that chooses a subset by
optimizing a multiwinner voting rule may be unbalanced in its selection -- it
may under or over represent a particular gender or political orientation in the
examples above. We introduce an algorithmic framework for multiwinner voting
problems when there is an additional requirement that the selected subset
should be "fair" with respect to a given set of attributes. Our framework
provides the flexibility to (1) specify fairness with respect to multiple,
non-disjoint attributes (e.g., ethnicity and gender) and (2) specify a score
function. We study the computational complexity of this constrained multiwinner
voting problem for monotone and submodular score functions and present several
approximation algorithms and matching hardness of approximation results for
various attribute group structure and types of score functions. We also present
simulations that suggest that adding fairness constraints may not affect the
scores significantly when compared to the unconstrained case.Comment: The conference version of this paper appears in IJCAI-ECAI 201
Optimal Procurement Contracts with Pre–Project Planning
The paper studies procurement contracts with pre–project investigations in the presence of adverse selection and moral hazard. To model the procurer’s roblem, we extend a standard sequential screening model to endogenous information acquisition with moral hazard. The optimal contract displays systematic distortions in information acquisition. Due to a rent effect, adverse selection induces too much information acquisition to prevent cost overruns and too little information acquisition to prevent false project cancelations. Moral hazard mitigates the distortions related to cost overruns yet exacerbates those related to false negatives. The optimal mechanism is a menu of option contracts that achieves the dual goal of providing incentives for information acquisition and truthful information revelation
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