83,507 research outputs found
Fair regression with wasserstein barycenters
We study the problem of learning a real-valued function that satisfies the Demographic Parity constraint. It demands the distribution of the predicted output to be independent of the sensitive attribute. We consider the case that the sensitive attribute is available for prediction. We establish a connection between fair regression and optimal transport theory, based on which we derive a close form expression for the optimal fair predictor. Specifically, we show that the distribution of this optimum is the Wasserstein barycenter of the distributions induced by the standard regression function on the sensitive groups. This result offers an intuitive interpretation of the optimal fair prediction and suggests a simple post-processing algorithm to achieve fairness. We establish risk and distribution-free fairness guarantees for this procedure. Numerical experiments indicate that our method is very effective in learning fair models, with a relative increase in error rate that is inferior to the relative gain in fairness
Addressing Fairness and Explainability in Image Classification Using Optimal Transport
Algorithmic Fairness and the explainability of potentially unfair outcomes
are crucial for establishing trust and accountability of Artificial
Intelligence systems in domains such as healthcare and policing. Though
significant advances have been made in each of the fields separately, achieving
explainability in fairness applications remains challenging, particularly so in
domains where deep neural networks are used. At the same time, ethical
data-mining has become ever more relevant, as it has been shown countless times
that fairness-unaware algorithms result in biased outcomes. Current approaches
focus on mitigating biases in the outcomes of the model, but few attempts have
been made to try to explain \emph{why} a model is biased. To bridge this gap,
we propose a comprehensive approach that leverages optimal transport theory to
uncover the causes and implications of biased regions in images, which easily
extends to tabular data as well. Through the use of Wasserstein barycenters, we
obtain scores that are independent of a sensitive variable but keep their
marginal orderings. This step ensures predictive accuracy but also helps us to
recover the regions most associated with the generation of the biases. Our
findings hold significant implications for the development of trustworthy and
unbiased AI systems, fostering transparency, accountability, and fairness in
critical decision-making scenarios across diverse domains
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
CapEst: A Measurement-based Approach to Estimating Link Capacity in Wireless Networks
Estimating link capacity in a wireless network is a complex task because the
available capacity at a link is a function of not only the current arrival rate
at that link, but also of the arrival rate at links which interfere with that
link as well as of the nature of interference between these links. Models which
accurately characterize this dependence are either too computationally complex
to be useful or lack accuracy. Further, they have a high implementation
overhead and make restrictive assumptions, which makes them inapplicable to
real networks.
In this paper, we propose CapEst, a general, simple yet accurate,
measurement-based approach to estimating link capacity in a wireless network.
To be computationally light, CapEst allows inaccuracy in estimation; however,
using measurements, it can correct this inaccuracy in an iterative fashion and
converge to the correct estimate. Our evaluation shows that CapEst always
converged to within 5% of the correct value in less than 18 iterations. CapEst
is model-independent, hence, is applicable to any MAC/PHY layer and works with
auto-rate adaptation. Moreover, it has a low implementation overhead, can be
used with any application which requires an estimate of residual capacity on a
wireless link and can be implemented completely at the network layer without
any support from the underlying chipset
Network coding meets TCP
We propose a mechanism that incorporates network coding into TCP with only
minor changes to the protocol stack, thereby allowing incremental deployment.
In our scheme, the source transmits random linear combinations of packets
currently in the congestion window. At the heart of our scheme is a new
interpretation of ACKs - the sink acknowledges every degree of freedom (i.e., a
linear combination that reveals one unit of new information) even if it does
not reveal an original packet immediately. Such ACKs enable a TCP-like
sliding-window approach to network coding. Our scheme has the nice property
that packet losses are essentially masked from the congestion control
algorithm. Our algorithm therefore reacts to packet drops in a smooth manner,
resulting in a novel and effective approach for congestion control over
networks involving lossy links such as wireless links. Our experiments show
that our algorithm achieves higher throughput compared to TCP in the presence
of lossy wireless links. We also establish the soundness and fairness
properties of our algorithm.Comment: 9 pages, 9 figures, submitted to IEEE INFOCOM 200
Fairness in Flowers: Campaign Toolkit
This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.ILRF_Flowers_ToolKit_2008.pdf: 511 downloads, before Oct. 1, 2020
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