24,291 research outputs found
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Rankings are the primary interface through which many online platforms match
users to items (e.g. news, products, music, video). In these two-sided markets,
not only the users draw utility from the rankings, but the rankings also
determine the utility (e.g. exposure, revenue) for the item providers (e.g.
publishers, sellers, artists, studios). It has already been noted that
myopically optimizing utility to the users, as done by virtually all
learning-to-rank algorithms, can be unfair to the item providers. We,
therefore, present a learning-to-rank approach for explicitly enforcing
merit-based fairness guarantees to groups of items (e.g. articles by the same
publisher, tracks by the same artist). In particular, we propose a learning
algorithm that ensures notions of amortized group fairness, while
simultaneously learning the ranking function from implicit feedback data. The
algorithm takes the form of a controller that integrates unbiased estimators
for both fairness and utility, dynamically adapting both as more data becomes
available. In addition to its rigorous theoretical foundation and convergence
guarantees, we find empirically that the algorithm is highly practical and
robust.Comment: First two authors contributed equally. In Proceedings of the 43rd
International ACM SIGIR Conference on Research and Development in Information
Retrieval 202
Magnification Control in Self-Organizing Maps and Neural Gas
We consider different ways to control the magnification in self-organizing
maps (SOM) and neural gas (NG). Starting from early approaches of magnification
control in vector quantization, we then concentrate on different approaches for
SOM and NG. We show that three structurally similar approaches can be applied
to both algorithms: localized learning, concave-convex learning, and winner
relaxing learning. Thereby, the approach of concave-convex learning in SOM is
extended to a more general description, whereas the concave-convex learning for
NG is new. In general, the control mechanisms generate only slightly different
behavior comparing both neural algorithms. However, we emphasize that the NG
results are valid for any data dimension, whereas in the SOM case the results
hold only for the one-dimensional case.Comment: 24 pages, 4 figure
Fairness of Exposure in Dynamic Recommendation
Exposure bias is a well-known issue in recommender systems where the exposure
is not fairly distributed among items in the recommendation results. This is
especially problematic when bias is amplified over time as a few items (e.g.,
popular ones) are repeatedly over-represented in recommendation lists and
users' interactions with those items will amplify bias towards those items over
time resulting in a feedback loop. This issue has been extensively studied in
the literature in static recommendation environment where a single round of
recommendation result is processed to improve the exposure fairness. However,
less work has been done on addressing exposure bias in a dynamic recommendation
setting where the system is operating over time, the recommendation model and
the input data are dynamically updated with ongoing user feedback on
recommended items at each round. In this paper, we study exposure bias in a
dynamic recommendation setting. Our goal is to show that existing bias
mitigation methods that are designed to operate in a static recommendation
setting are unable to satisfy fairness of exposure for items in long run. In
particular, we empirically study one of these methods and show that repeatedly
applying this method fails to fairly distribute exposure among items in long
run. To address this limitation, we show how this method can be adapted to
effectively operate in a dynamic recommendation setting and achieve exposure
fairness for items in long run. Experiments on a real-world dataset confirm
that our solution is superior in achieving long-term exposure fairness for the
items while maintaining the recommendation accuracy
Incorporating System-Level Objectives into Recommender Systems
One of the most essential parts of any recommender system is
personalization-- how acceptable the recommendations are from the user's
perspective. However, in many real-world applications, there are other
stakeholders whose needs and interests should be taken into account. In this
work, we define the problem of multistakeholder recommendation and we focus on
finding algorithms for a special case where the recommender system itself is
also a stakeholder. In addition, we will explore the idea of incremental
incorporation of system-level objectives into recommender systems over time to
tackle the existing problems in the optimization techniques which only look for
optimizing the individual users' lists.Comment: arXiv admin note: text overlap with arXiv:1901.0755
Fairguard: Harness Logic-based Fairness Rules in Smart Cities
Smart cities operate on computational predictive frameworks that collect,
aggregate, and utilize data from large-scale sensor networks. However, these
frameworks are prone to multiple sources of data and algorithmic bias, which
often lead to unfair prediction results. In this work, we first demonstrate
that bias persists at a micro-level both temporally and spatially by studying
real city data from Chattanooga, TN. To alleviate the issue of such bias, we
introduce Fairguard, a micro-level temporal logic-based approach for fair smart
city policy adjustment and generation in complex temporal-spatial domains. The
Fairguard framework consists of two phases: first, we develop a static
generator that is able to reduce data bias based on temporal logic conditions
by minimizing correlations between selected attributes. Then, to ensure
fairness in predictive algorithms, we design a dynamic component to regulate
prediction results and generate future fair predictions by harnessing logic
rules. Evaluations show that logic-enabled static Fairguard can effectively
reduce the biased correlations while dynamic Fairguard can guarantee fairness
on protected groups at run-time with minimal impact on overall performance.Comment: This paper was accepted by the 8th ACM/IEEE Conference on Internet of
Things Design and Implementatio
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