Filters








1,904 Hits in 9.7 sec

Recommendation or Discrimination?: Quantifying Distribution Parity in Information Retrieval Systems [article]

Rinat Khaziev, Bryce Casavant, Pearce Washabaugh, Amy A. Winecoff, and Matthew Graham
2019 arXiv   pre-print
Information retrieval (IR) systems often leverage query data to suggest relevant items to users.  ...  In this work, we introduce a statistical test for "distribution parity" in the top-K IR results, which assesses whether a given set of recommendations is fair with respect to a specific protected variable  ...  Application to Visual Search for Fashion Recommendations To provide an example of how our approach would function within a real-world information retrieval system, we apply our test for distribution parity  ... 
arXiv:1909.06429v1 fatcat:3mc2b4iyzfdatlz5cx4gkoweuu

Fairness in Information Access Systems [article]

Michael D. Ekstrand and Anubrata Das and Robin Burke and Fernando Diaz
2022 arXiv   pre-print
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  ...  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  ...  background and to lay out consistent terminology for our readers from information retrieval or recommender systems backgrounds.  ... 
arXiv:2105.05779v3 fatcat:fd35qmskibfbfaeblvmiqofgfe

Linear discriminant analysis for the discrimination of faults in bearing balls by using spectral features

Jinane Harmouche, Claude Delpha, Demba Diallo
2014 2014 First International Conference on Green Energy ICGE 2014  
An incipient fault is supposed to provoke an abnormal change in the measurements of the system variables.  ...  In this context, the Kullback-Leibler divergence is considered to be a 'global' fault indicator, which is recommended sensitive to abnormal small variations hidden in noise.  ...  The basic idea consists in rearranging the input-output or state-space system equations to yield parity equations allowing for decoupling the residual from the system states and decoupling among different  ... 
doi:10.1109/icge.2014.6835419 fatcat:rbkk7v5d6bdczdqor53z2do5je

Comparing Fair Ranking Metrics [article]

Amifa Raj, Michael D. Ekstrand
2022 arXiv   pre-print
Ranked lists are frequently used by information retrieval (IR) systems to present results believed to be relevant to the users information need.  ...  of IR system behavior.  ...  INTRODUCTION Ranked lists are frequently used in information retrieval (IR) to present items in response to users' information needs.  ... 
arXiv:2009.01311v2 fatcat:nl24kivp2fhftf3lo35qbq6sbm

Estimation of Fair Ranking Metrics with Incomplete Judgments [article]

Ömer Kırnap, Fernando Diaz, Asia Biega, Michael Ekstrand, Ben Carterette, Emine Yılmaz
2021 arXiv   pre-print
However, the protected attributes of individuals are rarely present, limiting the application of fair ranking metrics in large scale systems.  ...  There is increasing attention to evaluating the fairness of search system ranking decisions.  ...  ACKNOWLEDGMENTS This project was funded by the EPSRC Fellowship titled "Task Based Information Retrieval", grant reference number EP/P024289/1.  ... 
arXiv:2108.05152v1 fatcat:yr6idob6lvdvpk2ptpvp3fx6ie

Estimation of Fair Ranking Metrics with Incomplete Judgments

Ömer Kırnap, Fernando Diaz, Asia Biega, Michael Ekstrand, Ben Carterette, Emine Yilmaz
2021 Proceedings of the Web Conference 2021  
However, the protected attributes of individuals are rarely present, limiting the application of fair ranking metrics in large scale systems.  ...  There is increasing attention to evaluating the fairness of search system ranking decisions.  ...  ACKNOWLEDGMENTS This project was funded by the EPSRC Fellowship titled "Task Based Information Retrieval", grant reference number EP/P024289/1.  ... 
doi:10.1145/3442381.3450080 fatcat:w53apbxggne6pgu2o4roll5rt4

Fairness in Rankings and Recommendations: An Overview [article]

Evaggelia Pitoura, Kostas Stefanidis, Georgia Koutrika
2021 arXiv   pre-print
Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers  ...  In this work, we aim at presenting a toolkit of definitions, models and methods used for ensuring fairness in rankings and recommendations.  ...  Recommendation systems retrieve interesting items for users based on their profiles and their history.  ... 
arXiv:2104.05994v2 fatcat:4gl33dwz2zhw7nefekp6r3wlf4

A Survey of Research on Fair Recommender Systems [article]

Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogin, Alessandro Difonzo, Dario Zanzonelli
2022 arXiv   pre-print
Recommender systems can strongly influence which information we see online, e.g, on social media, and thus impact our beliefs, decisions, and actions.  ...  However, research on fairness in recommender systems is still a developing area.  ...  [4] discusses fairness aspects in the broader context of information access systems, an area which covers both information retrieval and recommender systems.  ... 
arXiv:2205.11127v2 fatcat:qcq5iuwlevg2dh54i4on5jj4hi

Fairness in Ranking, Part II: Learning-to-Rank and Recommender Systems

Meike Zehlike, Ke Yang, Julia Stoyanovich
2022 ACM Computing Surveys  
recommender systems communities.  ...  In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and  ...  This research was supported in part by NSF Awards No. 1934464, 1916505, and 1922658.  ... 
doi:10.1145/3533380 fatcat:4jhlthnoknhlde7hkd3bjbsphu

Fairness-Aware Ranking in Search Recommendation Systems with Application to LinkedIn Talent Search [article]

Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi
2019 arXiv   pre-print
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.  ...  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.  ...  ACKNOWLEDGMENTS The authors would like to thank other members of LinkedIn Careers and Talent Solutions teams for their collaboration while deploying our system in production, and in particular Patrick  ... 
arXiv:1905.01989v2 fatcat:kvfbi37jlbeg7cxjimnrkr3spy

Mitigating Bias in Algorithmic Systems - A Fish-Eye View

Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner-Tal, Alan Hartman, Tsvi Kuflik
2021 Zenodo  
Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences.  ...  as from the perspective of stakeholders in the broader context.  ...  In information retrieval systems, discrimination discovery is primarily used in user-focused studies.  ... 
doi:10.5281/zenodo.6240582 fatcat:vftoi4woebhrrp5tlmkclabgf4

Towards Fairness-aware Disaster Informatics: An Interdisciplinary Perspective

Y. Yang, C. Zhang, C. Fan, A. Mostafavi, X. Hu
2020 IEEE Access  
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation and National Academies  ...  ACKNOWLEDGMENT This material is based in part upon work supported by the National Science Foundation under Grant Number IIS-1759537 and CMMI-1846069 (CAREER).  ...  Compared to the significant influences enjoyed by traditional media as a source of information for situational awareness, the fairness of the AI-based recommendation systems they employ (e.g., information  ... 
doi:10.1109/access.2020.3035714 fatcat:xyofuz2qu5fcjn75tcipcrzolu

Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness [article]

Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis
2021 arXiv   pre-print
Modern decision-making systems that involve allocating resources or information to people (e.g., school choice, advertising) incorporate machine-learned predictions in their pipelines, raising concerns  ...  about potential strategic behavior or constrained allocation, concerns usually tackled in the context of mechanism design.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  ... 
arXiv:2010.05434v2 fatcat:d6dl3wgsf5fkrkirgpz7a5ul4q

Mitigating Bias in Algorithmic Systems - A Fish-Eye View

Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, BATSUREN KHUYAGBAATAR, Fausto GIUNCHIGLIA, Veronika Bogina, Avital Shulner Tal, Alan Hartman, Tsvi Kuflik
2022 Zenodo  
Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences.  ...  as from the perspective of stakeholders in the broader context.  ...  The proposed method incorporates fairness in a recommender or search system by choosing a sample of labeled images, based on gender when retrieving untagged images similar to an input image or query.  ... 
doi:10.5281/zenodo.6782985 fatcat:oc6qovumv5eszl5ukns4l3t6d4

Fairness of Exposure in Rankings

Ashudeep Singh, Thorsten Joachims
2018 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18  
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  ...  Rankings are ubiquitous in the online world today.  ...  Finally, we contrast fairness with the well-studied area of diversified ranking in information retrieval.  ... 
doi:10.1145/3219819.3220088 dblp:conf/kdd/SinghJ18 fatcat:vhb7gciumfeudk4sjrovcxmibi
« Previous Showing results 1 — 15 out of 1,904 results