Algorithms aren’t fair. Robin Burke wants to change that. [Press Release Colorado U]
The University of Colorado Boulder released a press release about the well-known recommender-systems researcher Robin Burke, titled “Algorithms aren’t fair. Robin Burke wants to change that”.
Scroll through an app on your phone looking for a song, movie or holiday gift, and an algorithm quietly hums in the background, applying data it’s gathered from you and other users to guess what you like.
But mounting research suggests these systems, known as recommender systems, can be biased in ways that leave out artists and other creators from underrepresented groups, reinforce stereotypes or foster polarization.
Armed with a new $930,000 grant from the National Science Foundation, University of Colorado Boulder Professor Robin Burke is working to change that.
“If a system takes the advantage that the winners in society already have and multiplies it, that increases inequality and means we have less diversity in art and music and movie making,” said Burke, chair of the Information Science Department at CU Boulder. “We have a lot to lose when recommender systems aren’t fair.”
https://www.colorado.edu/today/2021/11/11/algorithms-arent-fair-robin-burke-wants-change
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About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).