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A browser extension for users to gain back control over their YouTube recommendations.

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Recommender systems are the gateway to most of the content consumed on social media. These algorithms are opaque, not customizable, and designed to maximize advertising revenues rather than the interests of the users. On YouTube in particular, 70% of the content watched is suggested by an algorithm.We develop a browser extension, which enables users to customize, filter and enhance their recommendations when they visit YouTube.com.Our core design values is to provide customizable, transparent AI, trained on reliable and explicit human feedback, generated by our user-community.As an initial feature, the tool lets users filter-out unworthy clickbait content. With the extension downloaded, users see a button to flag content that they deem unworthy (clickbait), and the content that they like best (time well spent).These evaluations are then used to train our machine learning model in order to evaluate new content at scale, and to foster trusted, decentralized community of content reviewers.

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A browser extension for users to gain back control over their YouTube recommendations.

Contact

marc.faddoul@berkeley.edu

Endorsements
http://algotransparency.org
Disclaimer

We provide transparency on YouTube's algorithms

Country:  France

Status: Operational technology (practical and/or commercial viability)

Category: Decentralized solutions (including blockchain and distributed ledger technologies)

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