15,295 research outputs found
A Nutritional Label for Rankings
Algorithmic decisions often result in scoring and ranking individuals to
determine credit worthiness, qualifications for college admissions and
employment, and compatibility as dating partners. While automatic and seemingly
objective, ranking algorithms can discriminate against individuals and
protected groups, and exhibit low diversity. Furthermore, ranked results are
often unstable --- small changes in the input data or in the ranking
methodology may lead to drastic changes in the output, making the result
uninformative and easy to manipulate. Similar concerns apply in cases where
items other than individuals are ranked, including colleges, academic
departments, or products.
In this demonstration we present Ranking Facts, a Web-based application that
generates a "nutritional label" for rankings. Ranking Facts is made up of a
collection of visual widgets that implement our latest research results on
fairness, stability, and transparency for rankings, and that communicate
details of the ranking methodology, or of the output, to the end user. We will
showcase Ranking Facts on real datasets from different domains, including
college rankings, criminal risk assessment, and financial services.Comment: 4 pages, SIGMOD demo, 3 figuress, ACM SIGMOD 201
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
We present a framework for quantifying and mitigating algorithmic bias in
mechanisms designed for ranking individuals, typically used as part of
web-scale search and recommendation systems. We first propose complementary
measures to quantify bias with respect to protected attributes such as gender
and age. We then present algorithms for computing fairness-aware re-ranking of
results. For a given search or recommendation task, our algorithms seek to
achieve a desired distribution of top ranked results with respect to one or
more protected attributes. We show that such a framework can be tailored to
achieve fairness criteria such as equality of opportunity and demographic
parity depending on the choice of the desired distribution. We evaluate the
proposed algorithms via extensive simulations over different parameter choices,
and study the effect of fairness-aware ranking on both bias and utility
measures. We finally present the online A/B testing results from applying our
framework towards representative ranking in LinkedIn Talent Search, and discuss
the lessons learned in practice. Our approach resulted in tremendous
improvement in the fairness metrics (nearly three fold increase in the number
of search queries with representative results) without affecting the business
metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users
worldwide. Ours is the first large-scale deployed framework for ensuring
fairness in the hiring domain, with the potential positive impact for more than
630M LinkedIn members.Comment: This paper has been accepted for publication at ACM KDD 201
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
On Measuring Bias in Online Information
Bias in online information has recently become a pressing issue, with search
engines, social networks and recommendation services being accused of
exhibiting some form of bias. In this vision paper, we make the case for a
systematic approach towards measuring bias. To this end, we discuss formal
measures for quantifying the various types of bias, we outline the system
components necessary for realizing them, and we highlight the related research
challenges and open problems.Comment: 6 pages, 1 figur
Designing Fair Ranking Schemes
Items from a database are often ranked based on a combination of multiple
criteria. A user may have the flexibility to accept combinations that weigh
these criteria differently, within limits. On the other hand, this choice of
weights can greatly affect the fairness of the produced ranking. In this paper,
we develop a system that helps users choose criterion weights that lead to
greater fairness.
We consider ranking functions that compute the score of each item as a
weighted sum of (numeric) attribute values, and then sort items on their score.
Each ranking function can be expressed as a vector of weights, or as a point in
a multi-dimensional space. For a broad range of fairness criteria, we show how
to efficiently identify regions in this space that satisfy these criteria.
Using this identification method, our system is able to tell users whether
their proposed ranking function satisfies the desired fairness criteria and, if
it does not, to suggest the smallest modification that does. We develop
user-controllable approximation that and indexing techniques that are applied
during preprocessing, and support sub-second response times during the online
phase. Our extensive experiments on real datasets demonstrate that our methods
are able to find solutions that satisfy fairness criteria effectively and
efficiently
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
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