41,353 research outputs found
Proportional Rankings
In this paper we extend the principle of proportional representation to rankings. We consider the setting where alternatives need to be ranked based on approval preferences. In this setting, proportional representation requires that cohesive groups of voters are represented proportionally in each initial segment of the ranking. Proportional rankings are desirable in situations where initial segments of different lengths may be relevant, e.g., hiring decisions (if it is unclear how many positions are to be filled), the presentation of competing proposals on a liquid democracy platform (if it is unclear how many proposals participants are taking into consideration), or recommender systems (if a ranking has to accommodate different user types). We study the proportional representation provided by several ranking methods and prove theoretical guarantees. Furthermore, we experimentally evaluate these methods and present preliminary evidence as to which methods are most suitable for producing proportional rankings
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
Hierarchical orbital decompositions and extended decomposable distributions
Elliptically contoured distributions can be considered to be the
distributions for which the contours of the density functions are proportional
ellipsoids. Kamiya, Takemura and Kuriki (2006) generalized the elliptically
contoured distributions to star-shaped distributions, for which the contours
are allowed to be arbitrary proportional star-shaped sets. This was achieved by
considering the so-called orbital decomposition of the sample space in the
general framework of group invariance. In the present paper, we extend their
results by conducting the orbital decompositions in steps and obtaining a
further, hierarchical decomposition of the sample space. This allows us to
construct probability models and distributions with further independence
structures. The general results are applied to the star-shaped distributions
with a certain symmetric structure, the distributions related to the two-sample
Wishart problem and the distributions of preference rankings
Robustness of journal rankings by network flows with different amounts of memory
As the number of scientific journals has multiplied, journal rankings have
become increasingly important for scientific decisions. From submissions and
subscriptions to grants and hirings, researchers, policy makers, and funding
agencies make important decisions with influence from journal rankings such as
the ISI journal impact factor. Typically, the rankings are derived from the
citation network between a selection of journals and unavoidably depend on this
selection. However, little is known about how robust rankings are to the
selection of included journals. Here we compare the robustness of three journal
rankings based on network flows induced on citation networks. They model
pathways of researchers navigating scholarly literature, stepping between
journals and remembering their previous steps to different degree: zero-step
memory as impact factor, one-step memory as Eigenfactor, and two-step memory,
corresponding to zero-, first-, and second-order Markov models of citation flow
between journals. We conclude that higher-order Markov models perform better
and are more robust to the selection of journals. Whereas our analysis
indicates that higher-order models perform better, the performance gain for the
second-order Markov model comes at the cost of requiring more citation data
over a longer time period.Comment: 9 pages, 5 figure
Fair Allocation based on Diminishing Differences
Ranking alternatives is a natural way for humans to explain their
preferences. It is being used in many settings, such as school choice, course
allocations and residency matches. In some cases, several `items' are given to
each participant. Without having any information on the underlying cardinal
utilities, arguing about fairness of allocation requires extending the ordinal
item ranking to ordinal bundle ranking. The most commonly used such extension
is stochastic dominance (SD), where a bundle X is preferred over a bundle Y if
its score is better according to all additive score functions. SD is a very
conservative extension, by which few allocations are necessarily fair while
many allocations are possibly fair. We propose to make a natural assumption on
the underlying cardinal utilities of the players, namely that the difference
between two items at the top is larger than the difference between two items at
the bottom. This assumption implies a preference extension which we call
diminishing differences (DD), where X is preferred over Y if its score is
better according to all additive score functions satisfying the DD assumption.
We give a full characterization of allocations that are
necessarily-proportional or possibly-proportional according to this assumption.
Based on this characterization, we present a polynomial-time algorithm for
finding a necessarily-DD-proportional allocation if it exists. Using
simulations, we show that with high probability, a necessarily-proportional
allocation does not exist but a necessarily-DD-proportional allocation exists,
and moreover, that allocation is proportional according to the underlying
cardinal utilities. We also consider chore allocation under the analogous
condition --- increasing-differences.Comment: Revised version, based on very helpful suggestions of JAIR referees.
Gaps in some proofs were filled, more experiments were done, and mor
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
Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations
To help their users to discover important items at a particular time, major
websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K
recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most
Viewed News Stories), which rely on crowdsourced popularity signals to select
the items. However, different sections of a crowd may have different
preferences, and there is a large silent majority who do not explicitly express
their opinion. Also, the crowd often consists of actors like bots, spammers, or
people running orchestrated campaigns. Recommendation algorithms today largely
do not consider such nuances, hence are vulnerable to strategic manipulation by
small but hyper-active user groups.
To fairly aggregate the preferences of all users while recommending top-K
items, we borrow ideas from prior research on social choice theory, and
identify a voting mechanism called Single Transferable Vote (STV) as having
many of the fairness properties we desire in top-K item (s)elections. We
develop an innovative mechanism to attribute preferences of silent majority
which also make STV completely operational. We show the generalizability of our
approach by implementing it on two different real-world datasets. Through
extensive experimentation and comparison with state-of-the-art techniques, we
show that our proposed approach provides maximum user satisfaction, and cuts
down drastically on items disliked by most but hyper-actively promoted by a few
users.Comment: In the proceedings of the Conference on Fairness, Accountability, and
Transparency (FAT* '19). Please cite the conference versio
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