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SI Algo Bias & Fairness 2020 : Special Issue on Algorithmic Bias and Fairness in Search and Recommendation - Information Processing & Management

Submission Deadline: Nov 15, 2020
Notification of Acceptance: Jan 15, 2021
Camera Ready Version Due : Mar 15, 2021


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.

Call For Paper

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

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