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Bias 2020 : International Workshop on Algorithmic Bias in Search and Recommendation


When Apr 14, 2020 - Apr 14, 2020
Where Lisbon, Portugal
Submission Deadline Feb 3, 2020
Notification Due Mar 11, 2020
Final Version Due Mar 30, 2020
Categories    information retrieval   recommender systems   data and algorithmic bias   fairness

Call For Papers

Call for Papers

International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020)
to be held as part of the 42nd European Conference on Information Retrieval (ECIR 2020)
Workshop: April 14, 2020 - Lisbon, Portugal

Important Dates

Submissions: February 03, 2020 (EXTENDED)
Notifications: March 11, 2020 (EXTENDED)
Camera-Ready Contributions: March 30, 2020
Workshop: April 14, 2020 - Lisbon, Portugal

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

Workshop Aims and Scope

Search and recommendation are getting closer and closer as research areas. Though they require fundamentally different inputs, i.e., the user is asked to provide a query in search, while implicit and explicit feedback is leveraged in recommendation, existing search algorithms are being personalized based on users' profiles and recommender systems are optimizing their output on the ranking quality.

Both classes of algorithms aim to learn patterns from historical data that conveys biases in terms of imbalances and inequalities. These hidden biases are unfortunately captured in the learned patterns, and often emphasized in the results these algorithms provide to users. When a bias affects a sensitive attribute of a user, such as their gender or religion, the inequalities that are reinforced by search and recommendation algorithms even lead to severe societal consequences, like users discrimination.

For this critical reason, being able to detect, measure, characterize, and mitigate these biases while keeping high effectiveness is a prominent and timely topic for the IR community. Mitigating the effects generated by popularity bias, ensuring results that are fair with respect to the users, and being able to interpret why a model provides a given recommendation or search result are examples of challenges that may be important in real-world applications. This workshop aims to collect new contributions in this emerging field and to provide a common ground for interested researchers and practitioners.

Workshop Keywords

Information Retrieval · Recommender Systems · Data and Algorithmic Bias · Fairness

Workshop Topics

We solicit contributions in topics related to algorithmic bias 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 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
- Case Study Exploration:
--- E-commerce platforms
--- Educational environments
--- Entertainment websites
--- Healthcare systems
--- Social networks

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 the preparation of 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 (14 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.
- Short papers (8 pages) should introduce new point of views in the workshop topics or summarize the experience of a researcher or a group in the field. Practice and experience reports should present in detail real-world scenarios in which search and recommender systems are exploited.
- Demo papers (4 pages) should present a prototype or an application that employs search and recommender systems in the workshop topics. The systems will be shown at the workshop.

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 two reviews external to the organizing committee and one review internal to it, according to reviewers' expertise.

The accepted papers and the material generated during the meeting will be available on the workshop website. The workshop proceedings will be also published in a volume, whose details will be given soon, and indexed on DBLP and Scopus. Authors of selected papers may be invited to submit an extended version in a journal special issue.

Workshop Chairs

Ludovico Boratto, Eurecat - Centre Tecnológic de Catalunya (Spain)
Stefano Faralli, Unitelma Sapienza University of Rome (Italy)
Mirko Marras, University of Cagliari (Italy)
Giovanni Stilo, University of L’Aquila (Italy)

Program Committee (currently being updated)

Himan Abdollahpouri, University of Colorado Boulder (United States)
Luca Aiello, Nokia Bell Labs (United Kingdom)
Marcelo Armentano, Universidad Nacional del Centro de la Provincia de Buenos Aires (Argentina)
Alejandro Bellogin, Universidad Autónoma de Madrid (Spain)
Pasquale De Meo, University of Messina (Italy)
Carlotta Domeniconi, George Mason University (United States)
Michael Ekstrand, Boise State University (United States)
Francesco Fabbri, Universitat Pompeu Fabra (Spain)
Rossi Kamal, Kyung Hee University (South Korea)
Aonghus Lawlor, University College Dublin (Ireland)
Cataldo Musto, University of Bari Aldo Moro (Italy)
Fabrizio Silvestri, Facebook (United Kingdom)
Kyle Williams, Microsoft (United States)
Eva Zangerle, University of Innsbruck (Austria)
Meike Zehlike, Max Planck Institute (Germany)


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For general enquiries on the workshop, please send an email to,,, and

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KGSR 2021   Special Issue Knowledge Graphs for Search and Recommendation
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WSDM Demos 2021   Demonstrations @ Web Search and Data Mining
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