YouTube’s recommendations are still bad – says Mozilla
New research published today by Mozilla backs that notion up, suggesting YouTube’s AI continues to puff up piles of “bottom-feeding”/low-grade/divisive/disinforming content — stuff that tries to grab eyeballs by triggering people’s sense of outrage, sewing division/polarization or spreading baseless/harmful disinformation — which in turn implies that YouTube’s problem with recommending terrible stuff is indeed systemic; a side effect of the platform’s rapacious appetite to harvest views to serve ads.
That YouTube’s AI is still — per Mozilla’s study — behaving so badly also suggests Google has been pretty successful at fuzzing criticism with superficial claims of reform.
https://techcrunch.com/2021/07/07/youtubes-recommender-ai-still-a-horrorshow-finds-major-crowdsourced-study/
Big tech’s ‘blackbox’ [recommendation] algorithms face regulatory oversight under EU plans [Natasha Lomas @ TechCrunch]
Facebook Quietly Suspended Political Group Recommendations Ahead Of The US Presidential Election [BuzzFeed]
Mozilla is Crowdsourcing Research into (Harmful) YouTube Recommendations [Ashley Boyd @ Mozilla]
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).