Third International Workshop on Algorithmic Bias in Search and Recommendation

Bias 2022

Artificial Intelligence

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Call for Papers
Third International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2022)
to be held as part of the 44th European Conference on Information Retrieval (ECIR 2022)
Workshop: April 10, 2022 - Stavanger, Norway (with support for remote attendance)
Important Dates
Submissions: January 13, 2022
Notifications: February 13, 2022
Camera-Ready Contributions: March 3, 2022
Workshop: April 10, 2022 - Stavanger, Norway (with support for remote attendance)
All deadlines are 11:59pm, AoE time (Anywhere on Earth).
Workshop Aims and Scope
Creating search and recommendation algorithms that are efficient and effective has been the main objective for the industry and the academia for years. However, recent research has shown that these algorithms lead to models, trained on historical data, that might exacerbate existing biases and generate potentially negative outcomes. Defining, assessing and mitigating these biases throughout experimental pipelines is therefore a primary step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. In this workshop, we aim to collect novel contributions in this field and offer a common ground for interested researchers and practitioners.
Workshop Keywords
Information Retrieval · Recommender Systems · Data and Algorithmic Bias · Fairness
Workshop Topics
The workshop welcomes contributions in all topics related to algorithmic bias and fairness in search and recommendation, focused (but not limited) to:
Data Set Collection and Preparation:
- Studying the interplay between bias and imbalanced data or rare classes
- Designing methods for dealing with imbalances and inequalities in data
- Creating collection pipelines that lead to fair and less unbiased data sets
- Collecting data sets useful for the analysis of biased and unfair situations
- Designing collection protocols for data sets tailored to research on bias
Countermeasure Design and Development:
- Formalizing and operationalizing bias and fairness concepts
- Conducting exploratory analysis that uncover novel types of bias
- Designing treatments that mitigate biases in pre-/in-/post-processing
- Devising methods for explaining bias in search and recommendation
- Studying causal and counterfactual reasoning for bias and fairness
Evaluation Protocol and Metric Formulation:
- Performing auditing studies with respect to bias and fairness
- 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 mitigation strategies in unexplored domains
- Comparative studies of existing evaluation protocols and strategies
- Analysing efficiency and scalability issues of debiasing methods
Case Study Exploration:
- E-commerce platforms
- Educational environments
- Entertainment websites
- Healthcare systems
- Social media
- News platforms
- Digital libraries
- Job portals
- Dating platforms
Submission Details
All submissions must be written in English. Authors should consult ECIR paper guidelines ( and Fuhr’s guide to avoid common IR evaluation mistakes (, for preparing their papers. Authors should consult Springer’s authors’ guidelines ( and use their proceedings templates, either LaTeX ( or Word (
Papers should be submitted as PDF files to
Please be aware of the fact that at least one author per paper needs to register for the workshop and attend the workshop to present the work.
We will consider three different submission types:
- Full papers (12 pages) should be clearly placed with respect to the state of the art and state the contribution of the proposal in the domain of application, even if presenting preliminary results. In particular, research papers should describe the methodology in detail, experiments should be repeatable, and a comparison with the existing approaches in the literature should be made.
- Reproducibility papers (12 pages) should repeat prior experiments using the original source code and datasets to show how, why, and when the methods work or not (replicability papers) or should repeat prior experiments, preferably using the original source code, in new contexts (e.g., different domains and datasets, different evaluation and metrics) to further generalize and validate or not previous work (reproducibility papers).
- Short or position papers (6 pages) should introduce new point of views in the workshop topics or summarize the experience of a group in the field. Practice and experience reports should present in detail real-world scenarios in which search and recommender systems are exploited.
Submissions should not exceed the indicated number of pages, including any diagrams and references.
The reviewing process will be coordinated by the organizers. Each paper will receive three reviews from the programme committee, according to reviewers' expertise.
The accepted papers and the material generated during the meeting will be available on the workshop website. We plan to publish the workshop proceedings as a Springer's Communications in Computer and Information Science (CCIS) revised post-proceedings volume, indexed on Google Scholar, DBLP and Scopus. Authors of selected papers may be invited to submit an extended version in a journal special issue.
We expect authors, the program committee, and the organizing committee to adhere to the ACM’s Conflict of Interest Policy ( and the ACM’s Code of Ethics and Professional Conduct (
The registration will be managed by the Main Conference organization at Registration is yet to open.
Workshop Chairs
Ludovico Boratto
University of Cagliari, Cagliari, Italy
Stefano Faralli
Unitelma Sapienza University of Rome, Rome, Italy
Mirko Marras
University of Cagliari, Cagliari, Italy
Giovanni Stilo
University of L’Aquila, L’Aquila, Italy
For general enquiries on the workshop, please send an email to,,, and