TY - GEN
T1 - Beyond Algorithmic Fairness in Recommender Systems
AU - Elahi, Mehdi
AU - Abdollahpouri, Himan
AU - Mansoury, Masoud
AU - Torkamaan, Helma
PY - 2021
Y1 - 2021
N2 - Fairness is one of the crucial aspects of modern Recommender Systems which has recently drawn substantial attention from the community. Many recent works have addressed this aspect by studying the fairness of the recommendation through different forms of evaluation methodologies and metrics. However, the majority of these works have mainly concentrated on the recommendation algorithms and hence measured the fairness from the algorithmic viewpoint. While such viewpoint may still play an important role, it does not necessarily project a comprehensive picture of how the users may perceive the overall fairness of a recommender system. This paper extends the prior works and goes beyond the algorithmic fairness in recommender systems by highlighting the non-algorithmic viewpoint on the fairness in these systems. The paper proposes an evaluation methodology that can be used to assess the fairness of a recommender system perceived by its users. We have adopted a well-known model and re-formulated it to suit the particular characteristics of the recommender systems, and accordingly, their corresponding users. Our proposed methodology can be used in order to elicit the feedback of the users, along with three important dimensions, i.e., Engagement, Representation, and Action & Expression. We have formed a set of survey questions that address the aforementioned dimensions, as a set of examples to assess the fairness in a recommender system.
AB - Fairness is one of the crucial aspects of modern Recommender Systems which has recently drawn substantial attention from the community. Many recent works have addressed this aspect by studying the fairness of the recommendation through different forms of evaluation methodologies and metrics. However, the majority of these works have mainly concentrated on the recommendation algorithms and hence measured the fairness from the algorithmic viewpoint. While such viewpoint may still play an important role, it does not necessarily project a comprehensive picture of how the users may perceive the overall fairness of a recommender system. This paper extends the prior works and goes beyond the algorithmic fairness in recommender systems by highlighting the non-algorithmic viewpoint on the fairness in these systems. The paper proposes an evaluation methodology that can be used to assess the fairness of a recommender system perceived by its users. We have adopted a well-known model and re-formulated it to suit the particular characteristics of the recommender systems, and accordingly, their corresponding users. Our proposed methodology can be used in order to elicit the feedback of the users, along with three important dimensions, i.e., Engagement, Representation, and Action & Expression. We have formed a set of survey questions that address the aforementioned dimensions, as a set of examples to assess the fairness in a recommender system.
KW - evaluation
KW - fairness
KW - recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85107918389&partnerID=8YFLogxK
U2 - 10.1145/3450614.3461685
DO - 10.1145/3450614.3461685
M3 - Conference contribution
AN - SCOPUS:85107918389
T3 - UMAP 2021 - Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization
SP - 41
EP - 46
BT - UMAP 2021 - Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization
PB - Association for Computing Machinery (ACM)
T2 - 29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021
Y2 - 21 June 2020 through 25 June 2020
ER -