Bias 2021 : Second International Workshop on Algorithmic Bias in Search and Recommendation

The workshop welcomes contributions in all topics related to algorithmic biasand fairness in search and recommendation, focused (but not limited) to:

• 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 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)
  • 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