8,727 research outputs found
Fairness in Information Access Systems
Recommendation, information retrieval, and other information access systems
pose unique challenges for investigating and applying the fairness and
non-discrimination concepts that have been developed for studying other machine
learning systems. While fair information access shares many commonalities with
fair classification, the multistakeholder nature of information access
applications, the rank-based problem setting, the centrality of personalization
in many cases, and the role of user response complicate the problem of
identifying precisely what types and operationalizations of fairness may be
relevant, let alone measuring or promoting them.
In this monograph, we present a taxonomy of the various dimensions of fair
information access and survey the literature to date on this new and
rapidly-growing topic. We preface this with brief introductions to information
access and algorithmic fairness, to facilitate use of this work by scholars
with experience in one (or neither) of these fields who wish to learn about
their intersection. We conclude with several open problems in fair information
access, along with some suggestions for how to approach research in this space
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
Fairness of Exposure in Rankings
Rankings are ubiquitous in the online world today. As we have transitioned
from finding books in libraries to ranking products, jobs, job applicants,
opinions and potential romantic partners, there is a substantial precedent that
ranking systems have a responsibility not only to their users but also to the
items being ranked. To address these often conflicting responsibilities, we
propose a conceptual and computational framework that allows the formulation of
fairness constraints on rankings in terms of exposure allocation. As part of
this framework, we develop efficient algorithms for finding rankings that
maximize the utility for the user while provably satisfying a specifiable
notion of fairness. Since fairness goals can be application specific, we show
how a broad range of fairness constraints can be implemented using our
framework, including forms of demographic parity, disparate treatment, and
disparate impact constraints. We illustrate the effect of these constraints by
providing empirical results on two ranking problems.Comment: In Proceedings of the 24th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, London, UK, 201
Quantifying and Reducing Stereotypes in Word Embeddings
Machine learning algorithms are optimized to model statistical properties of
the training data. If the input data reflects stereotypes and biases of the
broader society, then the output of the learning algorithm also captures these
stereotypes. In this paper, we initiate the study of gender stereotypes in {\em
word embedding}, a popular framework to represent text data. As their use
becomes increasingly common, applications can inadvertently amplify unwanted
stereotypes. We show across multiple datasets that the embeddings contain
significant gender stereotypes, especially with regard to professions. We
created a novel gender analogy task and combined it with crowdsourcing to
systematically quantify the gender bias in a given embedding. We developed an
efficient algorithm that reduces gender stereotype using just a handful of
training examples while preserving the useful geometric properties of the
embedding. We evaluated our algorithm on several metrics. While we focus on
male/female stereotypes, our framework may be applicable to other types of
embedding biases.Comment: presented at 2016 ICML Workshop on #Data4Good: Machine Learning in
Social Good Applications, New York, N
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
Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison
Information access systems, such as search and recommender systems, often use ranked lists to present results believed to be relevant to the user\u27s information need. Evaluating these lists for their fairness along with other traditional metrics provides a more complete understanding of an information access system\u27s behavior beyond accuracy or utility constructs. To measure the (un)fairness of rankings, particularly with respect to the protected group(s) of producers or providers, several metrics have been proposed in the last several years. However, an empirical and comparative analyses of these metrics showing the applicability to specific scenario or real data, conceptual similarities, and differences is still lacking.
We aim to bridge the gap between theoretical and practical application of these metrics. In this paper we describe several fair ranking metrics from the existing literature in a common notation, enabling direct comparison of their approaches and assumptions, and empirically compare them on the same experimental setup and data sets in the context of three information access tasks. We also provide a sensitivity analysis to assess the impact of the design choices and parameter settings that go in to these metrics and point to additional work needed to improve fairness measurement
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