Fairness and Transparency in Ranking

Carlos Castillo, Pompeu Fabra University, Barcelona
29 October 2021, online
Seminar Series ‘Sense & Sensibility of AI

More and more, search engines not only research the information on the internet but also the clients asking them to search. This poses a whole new range of ethical questions in information retrieval, of which Prof. Castillo of Pompeu Fabra University will explain the latest research insights.

Ranking in Information Retrieval has been traditionally evaluated from the perspective of the relevance of search engine results to people searching for information, i.e., the extent to which the system provides “the right information, to the right people, in the right way, at the right time.” However, people in current Information Retrieval systems are not only the ones issuing search queries, but increasingly they are also the ones being searched.

Professor Castillo explains and expands on how this raises several new problems in Information Retrieval that have been addressed in recent research, particularly regarding fairness/non-discrimination, accountability, and transparency.

Carlos Castillo

Carlos Castillo is a Distinguished Research Professor at Universitat Pompeu Fabra in Barcelona, where he leads the Web Science and Social Computing research group. He is a web miner with a background on information retrieval, and has been influential in the areas of crisis informatics, web content quality and credibility, and adversarial web search.

Castillo is a prolific, highly cited researcher who has co-authored over 80 publications in top-tier international conferences and journals, receiving a test-of-time award, four best paper awards, and two best student paper awards. His works include a book on Big Crisis Data, as well as monographs on Information and Influence Propagation, and Adversarial Web Search.

Practical

  • 29 October 2021, 11-12h
  • Location: online
  • Contact: Laura Alonso
    laura.alonso@vlaamse-ai-academie.be
  • Language: English
  • Target audience: researchers with knowledge of the technical aspects of AI & machine learning

Registration?

  • Registration: until 28 October 2021
  • Prerequisites: M.Sc. degree
  • Price, free, but registration is required
  • Please check your spam folder if you did not receive the seminar link two days before the start of the seminar.

You registered, but cannot attend after all? Please unregister via a quick e-mail to info@vlaamse-ai-academie.be.

Sense & Sensibility of AI

Seminar series on AI Ethics:
Fairness, Privacy, Trustworthiness

AI has an increasing influence on our daily lives, examples include automated decision-making for high-stake decisions such as mortgages and loans, automated risk assessments for bail or recommenders on the internet. These AI systems carry the risk of creating filter bubbles and polarization. While AI is being rolled out into society, the discussion on how AI-based systems may align with and even affect our values, is pushed to the forefront. We gave the computer senses, but how can we give it sensibility? It requires a multi-disciplinary view, where both technical and non-technical perspectives have a prominent place.

In our lecture series ‘Sense & Sensibility of AI,’ we aim for Ph.D. Students to learn about the different aspects of Ethics in AI, not only to become aware of them but also to learn about the impact of AI on society and about methodologies to identify, assess, and possibly address ethical issues. The monthly seminars tackle subjects such as bias and fairness, privacy, trustworthiness, balancing technical, social, and regulatory perspectives.

The series is targeted towards doctoral students working in the broad field of AI and data science. To understand the lectures in full, it may be required to have a background in the technical aspects of AI/machine learning.

Sense & Sensibility of AI is a seminar series developed by Flemish AI Academy in collaboration and with the support of all our partners, all universities in Flanders, and Knowledge Center Data & Society.

Register