Recommender Systems for a Fair and Sustainable Future

Speaker:  Alan Said – Gothenburg, Sweden
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science

Abstract

Recommender systems have become central to digital decision-making, shaping what we see, buy, and engage with. But as their influence grows, so too do the societal and environmental consequences of their design and deployment. This talk explores the role of recommender systems in promoting — or undermining — fairness, accountability, and sustainability. Drawing on recent research, we examine the carbon footprint of deep learning-based recommenders, the complexity of defining fairness in practice, and the need for transparent, multi-stakeholder approaches. Through critical questions and real-world examples, we challenge the community to rethink what it means to build recommenders "for good" — and consider what a truly fair and sustainable future for these systems might look like.

About this Lecture

Number of Slides:  80
Duration:  45 - 80 minutes
Languages Available:  English
Last Updated: 

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