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  4. The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems
 
conference paper

The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems

Gomez, Elizabeth
•
Zhang, Carlos Shui
•
Boratto, Ludovico
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January 1, 2021
Sigir '21 - Proceedings Of The 44Th International Acm Sigir Conference On Research And Development In Information Retrieval
44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Educational recommender systems channel most of the research efforts on the effectiveness of the recommended items. While teachers have a central role in online platforms, the impact of recommender systems for teachers in terms of the exposure such systems give to the courses is an under-explored area. In this paper, we consider data coming from a real-world platform and analyze the distribution of the recommendations w.r.t. the geographical provenience of the teachers. We observe that data is highly imbalanced towards the United States, in terms of offered courses and of interactions. These imbalances are exacerbated by recommender systems, which overexpose the countryw.r.t. its representation in the data, thus generating unfairness for teachers outside that country. To introduce equity, we propose an approach that regulates the share of recommendations given to the items produced in a country (visibility) and the position of the items in the recommended list (exposure).

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Type
conference paper
DOI
10.1145/3404835.3463235
Web of Science ID

WOS:000719807900199

Author(s)
Gomez, Elizabeth
Zhang, Carlos Shui
Boratto, Ludovico
Salamo, Maria
Marras, Mirko  
Date Issued

2021-01-01

Publisher

ASSOC COMPUTING MACHINERY

Publisher place

New York

Published in
Sigir '21 - Proceedings Of The 44Th International Acm Sigir Conference On Research And Development In Information Retrieval
ISBN of the book

978-1-4503-8037-9

Start page

1808

End page

1812

Subjects

Computer Science, Information Systems

•

Computer Science

•

mooc

•

education

•

online course

•

fairness

•

bias

•

data imbalance

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
ML4ED  
Event nameEvent placeEvent date
44th International ACM SIGIR Conference on Research and Development in Information Retrieval

ELECTR NETWORK

Jul 11-15, 2021

Available on Infoscience
January 1, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/184176
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