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

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