1st Workshop on

Biased Data in Conversational Agents

ECML-PKDD | 18-22 September 2023

Conversational Agents (CA) are now widely prevalent, found in various aspects of our daily interactions, including computers (e.g., Cortana), smartphones (e.g., Siri), homes, and websites. These systems may be rule based or statistical based machine learning models, and in the latter case, CAs are trained on conversation corpora that are either gathered in real life settings or created using the Wizard-Of-Oz method. When the corpora are gathered from real life sources like online platforms such as Reddit, the large variety of data ensures generalization in the CA responses, but it may introduce implicit or explicit biases related to racial, sexual, political, or gender matters. The presence of such biases in the data poses a challenge to the construction of CAs, as it may amplify the potential risks that biases pose to society. Therefore, it is crucial to ensure that CAs exhibit responsible and safe behavior in their interactions with users.

Workshop Objectives

The aim of this workshop is to address the challenges posed by biased data in both Machine Learning and society. We invite researchers to compare different chatbots/corpora on sexual, political, racist, or gender topics; study methods to create or mitigate bias during the construction of datasets; define approaches to assess and/or remove bias present in the corpora; or handle bias at the chatbot level through NLP or Machine Learning techniques.

We also welcome submissions that take a theoretical approach to addressing the issue of bias in CAs, without necessarily involving the creation of a corpus, implementation of an agent, or exploration of Machine Learning techniques.

Topics of Interest

The workshop solicits contributions including but not limited to the following topics

  • Comparison and Evaluation of Corpora/Conversational Agents on biased data
  • Assessing and mitigating biased data in corpora
  • Personalized NLP and Information Retrieval
  • Sexist, Racist, Political and Gender Dictionary and Ontology
  • NLP and Machine Learning methods to recognize and handle biased data
  • Impact of biased data on Conversational Agents
  • Topic recognition and repair strategies in biased conversations
  • Mental Models for biased data
  • Corpora creation and corpora annotation (automatic methods are accepted)

Submission Instructions

Authors are invited to submit original, previously unpublished research papers.
We encourage the submission of:

  • extended abstracts: 2 pages,
  • short papers: between 5 to 7 pages,
  • regular papers: between 8 to 14 pages.
The space for references is unlimited.

Abstracts and papers must be written in English and formatted according to the Springer LNCS guidelines. Author instructions, style files, and the copyright form can be downloaded here. All papers must be converted to PDF prior to electronic submission.

All papers need to be ‘best-effort’ anonymized. We strongly encourage making code and data available anonymously (e.g., in an anonymous GitHub repository via Anonymous GitHub or in a Dropbox folder). The authors may have a (non-anonymous) pre-print published online, but it should not be cited in the submitted paper to preserve anonymity. Reviewers will be asked not to search for them.

At least one author of each accepted paper must have a full registration and be in-presence to present the paper. Papers without a full registration or in-presence presentation won't be included in the post-workshop Springer proceedings.

Select: Biased Data in Conversational Agents

Important Dates

  • Paper Submission Deadline: 12 June 2023 extended to 23 June 2023
  • Paper Author Notification: 12 July 2023
  • Paper Camera Ready: TBA

Invited Speakers


Organizing Committee

Francesca Grasso

Francesca Grasso

University of Turin, Italy


Giovanni Siragusa

Giovanni Siragusa

University of Turin, Italy


Program Committee Members

  • Kolawole Adebayo - Adapt Center, Ireland
  • Federica Cena - University of Turin, Italy
  • Luigi Di Caro - University of Turin, Italy
  • Shohreh Haddadan - Zortify, Luxembourg
  • Justin Edwards - University of Oulu, Finland
  • Michael Fell - Zortify, Luxembourg
  • Davide Liga - University of Luxembourg, Luxembourg
  • Alessandro Mazzei - University of Turin, Italy
  • Emmanuel Papadakis - University of Huddersfield, United Kingdom
  • Livio Robaldo - Swansea University, Wales
  • Marco Viviani - University of Milan - Bicocca, Italy