814 research outputs found
Operationalizing Individual Fairness with Pairwise Fair Representations
We revisit the notion of individual fairness proposed by Dwork et al. A
central challenge in operationalizing their approach is the difficulty in
eliciting a human specification of a similarity metric. In this paper, we
propose an operationalization of individual fairness that does not rely on a
human specification of a distance metric. Instead, we propose novel approaches
to elicit and leverage side-information on equally deserving individuals to
counter subordination between social groups. We model this knowledge as a
fairness graph, and learn a unified Pairwise Fair Representation (PFR) of the
data that captures both data-driven similarity between individuals and the
pairwise side-information in fairness graph. We elicit fairness judgments from
a variety of sources, including human judgments for two real-world datasets on
recidivism prediction (COMPAS) and violent neighborhood prediction (Crime &
Communities). Our experiments show that the PFR model for operationalizing
individual fairness is practically viable.Comment: To be published in the proceedings of the VLDB Endowment, Vol. 13,
Issue.
Exploring explanations for matrix factorization recommender systems (Position Paper)
In this paper we address the problem of finding explanations for collaborative filtering algorithms that use matrix factorization methods. We look for explanations that increase the transparency of the system. To do so, we propose two measures. First, we show a model that describes the contribution of each previous rating given by a user to the generated recommendation. Second, we measure then influence of changing each previous rating of a user on the outcome of the recommender system. We show that under the assumption that there are many more users in the system than there are items, we can efficiently generate each type of explanation by using linear approximations of the recommender system’s behavior for each user, and computing partial derivatives of predicted ratings with respect to each user’s provided ratings.http://scholarworks.boisestate.edu/fatrec/2017/1/7/Published versio
Quantifying Information Overload in Social Media and its Impact on Social Contagions
Information overload has become an ubiquitous problem in modern society.
Social media users and microbloggers receive an endless flow of information,
often at a rate far higher than their cognitive abilities to process the
information. In this paper, we conduct a large scale quantitative study of
information overload and evaluate its impact on information dissemination in
the Twitter social media site. We model social media users as information
processing systems that queue incoming information according to some policies,
process information from the queue at some unknown rates and decide to forward
some of the incoming information to other users. We show how timestamped data
about tweets received and forwarded by users can be used to uncover key
properties of their queueing policies and estimate their information processing
rates and limits. Such an understanding of users' information processing
behaviors allows us to infer whether and to what extent users suffer from
information overload.
Our analysis provides empirical evidence of information processing limits for
social media users and the prevalence of information overloading. The most
active and popular social media users are often the ones that are overloaded.
Moreover, we find that the rate at which users receive information impacts
their processing behavior, including how they prioritize information from
different sources, how much information they process, and how quickly they
process information. Finally, the susceptibility of a social media user to
social contagions depends crucially on the rate at which she receives
information. An exposure to a piece of information, be it an idea, a convention
or a product, is much less effective for users that receive information at
higher rates, meaning they need more exposures to adopt a particular contagion.Comment: To appear at ICSWM '1
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
People are rated and ranked, towards algorithmic decision making in an
increasing number of applications, typically based on machine learning.
Research on how to incorporate fairness into such tasks has prevalently pursued
the paradigm of group fairness: giving adequate success rates to specifically
protected groups. In contrast, the alternative paradigm of individual fairness
has received relatively little attention, and this paper advances this less
explored direction. The paper introduces a method for probabilistically mapping
user records into a low-rank representation that reconciles individual fairness
and the utility of classifiers and rankings in downstream applications. Our
notion of individual fairness requires that users who are similar in all
task-relevant attributes such as job qualification, and disregarding all
potentially discriminating attributes such as gender, should have similar
outcomes. We demonstrate the versatility of our method by applying it to
classification and learning-to-rank tasks on a variety of real-world datasets.
Our experiments show substantial improvements over the best prior work for this
setting.Comment: Accepted at ICDE 2019. Please cite the ICDE 2019 proceedings versio
{iFair}: {L}earning Individually Fair Data Representations for Algorithmic Decision Making
People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting
Index Coding: Rank-Invariant Extensions
An index coding (IC) problem consisting of a server and multiple receivers
with different side-information and demand sets can be equivalently represented
using a fitting matrix. A scalar linear index code to a given IC problem is a
matrix representing the transmitted linear combinations of the message symbols.
The length of an index code is then the number of transmissions (or
equivalently, the number of rows in the index code). An IC problem is called an extension of another IC problem if the
fitting matrix of is a submatrix of the fitting matrix of . We first present a straightforward \textit{-order} extension
of an IC problem for which an index code is
obtained by concatenating copies of an index code of . The length
of the codes is the same for both and , and if the
index code for has optimal length then so does the extended code for
. More generally, an extended IC problem of having
the same optimal length as is said to be a \textit{rank-invariant}
extension of . We then focus on -order rank-invariant extensions
of , and present constructions of such extensions based on involutory
permutation matrices
Optimal Index Codes via a Duality between Index Coding and Network Coding
In Index Coding, the goal is to use a broadcast channel as efficiently as
possible to communicate information from a source to multiple receivers which
can possess some of the information symbols at the source as side-information.
In this work, we present a duality relationship between index coding (IC) and
multiple-unicast network coding (NC). It is known that the IC problem can be
represented using a side-information graph (with number of vertices
equal to the number of source symbols). The size of the maximum acyclic induced
subgraph, denoted by is a lower bound on the \textit{broadcast rate}.
For IC problems with and , prior work has shown that
binary (over ) linear index codes achieve the lower bound
for the broadcast rate and thus are optimal. In this work, we use the the
duality relationship between NC and IC to show that for a class of IC problems
with , binary linear index codes achieve the lower bound on
the broadcast rate. In contrast, it is known that there exists IC problems with
and optimal broadcast rate strictly greater than
Equity of Attention: Amortizing Individual Fairness in Rankings
Rankings of people and items are at the heart of selection-making,
match-making, and recommender systems, ranging from employment sites to sharing
economy platforms. As ranking positions influence the amount of attention the
ranked subjects receive, biases in rankings can lead to unfair distribution of
opportunities and resources, such as jobs or income.
This paper proposes new measures and mechanisms to quantify and mitigate
unfairness from a bias inherent to all rankings, namely, the position bias,
which leads to disproportionately less attention being paid to low-ranked
subjects. Our approach differs from recent fair ranking approaches in two
important ways. First, existing works measure unfairness at the level of
subject groups while our measures capture unfairness at the level of individual
subjects, and as such subsume group unfairness. Second, as no single ranking
can achieve individual attention fairness, we propose a novel mechanism that
achieves amortized fairness, where attention accumulated across a series of
rankings is proportional to accumulated relevance.
We formulate the challenge of achieving amortized individual fairness subject
to constraints on ranking quality as an online optimization problem and show
that it can be solved as an integer linear program. Our experimental evaluation
reveals that unfair attention distribution in rankings can be substantial, and
demonstrates that our method can improve individual fairness while retaining
high ranking quality.Comment: Accepted to SIGIR 201
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