BIAS 2024 : International Workshop on Algorithmic Bias in Search and Recommendation
BIAS 2024 : International Workshop on Algorithmic Bias in Search and Recommendation

BIAS 2024 : International Workshop on Algorithmic Bias in Search and Recommendation

Washington D.C.
Event Date: July 18, 2024 - July 18, 2024
Submission Deadline: April 25, 2024
Notification of Acceptance: June 06, 2024
Camera Ready Version Due: June 20, 2024

Call for Papers

Please accept our apologies in case of multiple receptions.
Please send to interested colleagues and students.

Call for Papers

International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2024)

to be held as part of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024)

Workshop: July 18, 2024 - Washington D.C., USA


Important Dates


Submissions: April 25, 2024

Notifications: June 6, 2024

Camera-Ready: June 20, 2024

Workshop: July 18, 2024 - Washington D.C., USA

All deadlines are 11:59pm, AoE time (Anywhere on Earth).


Workshop Aims and Scope


Creating efficient and effective search and recommendation algorithms has been the main objective of industry practitioners and academic researchers over the years. However, recent research has shown how these algorithms trained on historical data lead to models that might exacerbate existing biases and generate potentially negative outcomes. Defining, assessing, and mitigating these biases throughout experimental pipelines is a primary step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. This workshop aims to collect novel contributions in this field and offer a common ground for interested researchers and practitioners.


Workshop Keywords


Bias · Fairness · Information Retrieval · Recommender Systems


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.
- Designing methods for dealing with imbalances in data.
- Creating data pipelines for less biased data sets.
- Collecting data sets for the analysis of biased situations.
- Designing protocols for data sets tailored to bias analysis.

Countermeasure Design and Development:
- Formalizing and operationalizing bias concepts.
- Conducting exploratory analysis that uncover bias.
- Designing treatments that mitigate biases.
- Devising methods for explaining biases.
- Studying causal and counterfactual reasoning for bias.

Evaluation Protocol and Metric Formulation:
- Performing auditing studies with respect to bias.
- Conducting experimental studies on bias.
- Defining objective metrics that consider bias.
- Formulating bias-aware protocols to evaluate models.
- Evaluating mitigation strategies in unexplored domains.
- Comparative studies of existing evaluation protocols.
- Analysing 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


We invite authors to submit unpublished original papers, written in English. Submitted papers should not have been previously published or accepted for publication in substantially similar form in any peer-reviewed venue, such as journals, conferences, or workshops.

The authors should consult the Springer’s authors’ guidelines ( and use their proceedings templates, either LaTeX ( or Word (

Papers should be submitted as PDF files to

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 paper (6 pages) or position papers (4 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.

All submissions will go through a double-blind review process and be reviewed by at least three reviewers on the basis of relevance for the workshop, novelty/originality, significance, technical quality and correctness, quality and clarity of presentation, quality of references and reproducibility.

Submitted papers will be rejected without review in case they are not properly anonymized, do not comply with the template, or do not follow the above guidelines.

The accepted papers and the material generated during the meeting will be available on the workshop website. It is planned to send the workshop proceedings for consideration for inclusion as a Springer's Communications in Computer and Information Science (CCIS) revised post-proceedings volume, indexed on Google Scholar, DBLP and Scopus. The authors of selected papers may be invited to submit an extended version in a journal special issue.

Please be aware that at least one author per paper needs to register and attend the workshop to present the work.

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 SIGIR 2024 Conference organization at Registration is already open.


Workshop Chairs


- Alejandro Bellogín, Universidad Autónoma de Madrid, Madrid, Spain
- Ludovico Boratto, University of Cagliari, Cagliari, Italy
- Styliani Kleanthous, Open University of Cyprus, CYENS Centre of Excellence Nicosia, Cyprus
- Elisabeth Lex, Graz University of Technology Graz, Austria
- Francesca Maridina Malloci, University of Cagliari, Cagliari, Italy
- Mirko Marras, University of Cagliari Cagliari, Italy




For general enquiries on the workshop, please send an email to [email protected], [email protected], [email protected], [email protected], [email protected], [email protected].


BIAS 2024 : International Workshop on Algorithmic Bias in Search and Recommendation will take place in Washington D.C.. It’s a 1 day event starting on Jul 18, 2024 (Thursday) and will be winded up on Jul 18, 2024 (Thursday).

BIAS 2024 falls under the following areas: BIAS, FAIRNESS, INFORMATION RETRIEVAL, RECOMMENDER SYSTEMS, etc. Submissions for this Workshop can be made by Apr 25, 2024. Authors can expect the result of submission by Jun 6, 2024. Upon acceptance, authors should submit the final version of the manuscript on or before Jun 20, 2024 to the official website of the Workshop.

Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines.

Other Details of the BIAS 2024

  • Short Name: BIAS 2024
  • Full Name: International Workshop on Algorithmic Bias in Search and Recommendation
  • Timing: 09:00 AM-06:00 PM (expected)
  • Fees: Check the official website of BIAS 2024
  • Event Type: Workshop
  • Website Link:
  • Location/Address: Washington D.C.

Credits and Sources

[1] BIAS 2024 : International Workshop on Algorithmic Bias in Search and Recommendation

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