Lead by Fundació Eurecat

Place and registration: Online

This session would pose a particular focus on the potential harmful consequences generated by the injection of the human-biases into AI, which lead to the so-called “Algorithmic Bias”. The main objectives of the session are: 1) describe the role of algorithmic bias embedded in machine learning applications (e.g. recommender systems, rankings, NLP, computer vision, etc..); 2) present methods able to analyze and mitigate those biases, with an emphasis on recommender systems and rankings.

The session will be covering the following topics:

  • introduction of algorithmic bias in AI (e.g. algorithmic unfairness, polarization)
  • auditing algorithms for detecting harmful consequences of this bias, with an emphasis on recommender systems
  • mitigation strategies to reduce unfairness in recommendation and ranking systems.

All the online activities of Data Week are free of charge