Algorithmic Bias and Fairness in Search and Recommendation

  in Special Issue   Posted on May 7, 2020

Information for the Special Issue

Special Issue Call for Papers:

Over the aspects getting special attention in search and recommendation so far, the capability to uncover, characterize, and counteract data and algorithmic biases, while preserving the original level of accuracy, is proving to be prominent and timely. Both classes of algorithms are trained on historical data, which often conveys imbalances and inequalities. Such patterns in the training data might be captured and emphasized in the results these algorithms provide to users, leading to biased or even unfair decisions. This can happen when an algorithm systematically discriminates users as individuals or as belonging to a legally-protected class, identified by common sensitive attributes.

Given the increasing adoption of systems empowered with search and recommendation capabilities, it is crucial to ensure that their decisions do not lead to biased or even discriminatory outcomes. Controlling the effects generated by popularity bias to improve the user’s perceived quality of the results, supporting consumers and providers with fair rankings and recommendations, and providing transparent results are examples of challenges that require attention. This special issue intends to bring together original research methods and applications that put people first, inspect social and ethical impacts, and uplift the public’s trust on search and recommendation technologies. The goal is to favor a community-wide dialogue on new research perspectives in this field.

 

Topics

We solicit different types of contributions (research papers, surveys, replicability and reproducibility studies, resource papers, systematic review articles) on algorithmic bias in search and recommendation, focused but not limited to the following areas. If in doubt about the suitability, please contact the Guest Editors.

Data Set Collection and Preparation:

○ Managing imbalances and inequalities within data sets

○ Devising collection pipelines that lead to fair and unbiased data sets

○ Collecting data sets useful for studying potential biased and unfair situations

○ Designing procedures for creating synthetic data sets for research on bias and fairness

Countermeasure Design and Development:

○ Conducting exploratory analysis that uncover biases

○ Designing treatments that mitigate biases (e.g., popularity bias mitigation)

○ Devising interpretable search and recommendation models

○ Providing treatment procedures whose outcomes are easily interpretable

○ Balancing inequalities among different groups of users or stakeholders

Evaluation Protocol and Metric Formulation:

○ Conducting quantitative experimental studies on bias and unfairness

○ Defining objective metrics that consider fairness and/or bias

○ Formulating bias-aware protocols to evaluate existing algorithms

○ Evaluating existing strategies in unexplored domains

○ Comparative studies of existing evaluation protocols and strategies

Case Study Exploration:

○ E-commerce platforms

○ Educational environments

○ Entertainment websites

○ Healthcare systems

○ Social media

○ News platforms

○ Digital libraries

○ Job portals

○ Dating platforms

 

Submission Guidelines

Prospective authors should submit original manuscripts that have not appeared, nor are under consideration, in any other journals or venue. Submissions must be prepared according to the Journal submission guidelines: https://www.elsevier.com/journals/information-processing-and-management/0306-4573/guide-for-authors. Papers should be submitted electronically using the Elsevier submission system to Information Processing & Management (https://www.editorialmanager.com/IPM/default.aspx) and following the Journal submission guidelines. When submitting the manuscript please select the article type “SI: Algo Bias & Fairness” as the Article Type, to ensure your manuscript is correctly assigned. Please submit your manuscript before the submission deadline. Novelty, significance, technical soundness, clarity, and appropriateness with respect to the special issue topics will be considered during reviews.

 

Important Dates

  • Submission system open: May 15, 2020
  • Manuscript submission due: November 15, 2020
  • First round decision made: January 15, 2021
  • Revised manuscript due: March 15, 2021
  • Final decision made: May 15, 2021
  • Final paper due: June 15, 2021

Note – IP&M adheres to a rolling submission model, with the full special issue targeted for June 2021.

 

Guest Editors

Dr. Ludovico Boratto, Data Science and Big Data Analytics Research Group, Eurecat – Centre Tecnològic de Catalunya, Barcelona, Spain. Email: ludovico.boratto@acm.org

Dr. Stefano Faralli, Unitelma Sapienza University of Rome, Rome, Italy. Email: stefano.faralli@unitelmasapienza.it

Dr. Mirko Marras, Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy. Email: mirko.marras@unica.it

Dr. Giovanni Stilo, Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy. Email: giovanni.stilo@univaq.it

 

A Brief CV of the Guest Editors

Dr. Ludovico Boratto (https://www.ludovicoboratto.com/) is senior research scientist in the Data Science and Big Data Analytics research group at Eurecat, in Barcelona (Spain). His research interests focus on recommender systems and on their impact on the different stakeholders, both considering accuracy and beyond-accuracy evaluation metrics. The results of his research have been published in top-tier conferences and journals. His research activity also brought him to give talks and tutorials at top-tier conferences (e.g., ACM RecSys 2016, IEEE ICDM 2017) and research centers (Yahoo! Research). He is editor of the book “Group Recommender Systems: An Introduction”, published by Springer. He is an editorial board member of the “Information Processing & Management” journal (Elsevier) and guest editor of several journal’s special issues. He is regularly part of the program committee of the main Data Mining and Web conferences, such as RecSys, KDD, SIGIR, WSDM, ICWSM, and TheWebConf. In 2012, he got a Ph.D. at the University of Cagliari (Italy), where he was research assistant until May 2016. In 2010 and 2014 he spent 10 months at Yahoo! Research in Barcelona as a visiting researcher. He is a member of the ACM and of the IEEE.

Dr. Stefano Faralli (https://scholar.google.com/citations?user=3SGt4IEAAAAJ&hl=en) is an assistant professor at University of Rome Unitelma Sapienza, Rome, Italy. His research interests include Ontology Learning, Distributional Semantics, Word Sense Disambiguation/Induction, Recommender Systems, Linked Open Data. He co-organized the International Workshop: Taxonomy Extraction Evaluation (TexEval) Task 17 of Semantic Evaluation (SemEval-2015) and the International Workshop on Social Interaction-based Recommendation (SIR 2018).

Dr. Mirko Marras (http://www.mirkomarras.com/) is a Postdoctoral Researcher in Computer Science at the Department of Mathematics and Computer Science of the University of Cagliari (Italy). His research interests focus on machine and deep Learning approaches mostly applied on knowledge-aware systems, recommender systems, biometric systems, and opinion mining systems. He has authored papers in top-tier international journals, such as Pattern Recognition Letters (Elsevier), Computers in Human Behavior (Elsevier), and IEEE Cloud Computing. He has given talks and demos at several international conferences and workshops, such as TheWebConf 2018, ECIR 2019, and INTERSPEECH 2019. He has been involved as a PC member in major conferences such as ACL, AIED, EDM, ECML-PKDD, ITICSE, ICALT, UMAP. He has been co-chairing the “International Workshop on Algorithmic Bias in Search and Recommendation” at ECIR 2020 and has been giving an hands-on tutorial on “Data and Algorithmic Bias in Recommender Systems” at UMAP2020. In 2020, he received a PhD Degree in Computer Science from University of Cagliari. In 2017 and 2018, he spent six months at EURECAT. He has also spent two months at New York University in 2019. He is a member of several national and international associations, including CVPL, AIxIA, IEEE, and ACM.

Dr. Giovanni Stilo (https://scholar.google.com/citations?user=uTyaicMAAAAJ&hl=en) is an Assistant Professor in the Department of Information Engineering, Computer Science and Mathematics at the University of L’Aquila. He received his PhD. in Computer Science in 2013, and in 2014 he was a visiting researcher at Yahoo! Labs in Barcelona. Between 2015 and 2018, he was a researcher in the Computer Science Department at La Sapienza University, in Rome. His research interests are in the areas of machine learning and data mining, and specifically temporal mining, social network analysis, network medicine, semantics-aware recommender systems, and anomaly detection. He has organized several international workshops, held in conjunction with top-tier conferences (ICDM, CIKM, and ECIR), and he is involved as editor and reviewer of top-tier journals, such as TITS, TKDE, DMKD, AI, KAIS, and AIIM.

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