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.