Abstract

The internet is fundamentally changing how we socialize, work, or gather information. The recent emergence of content serving services creates a new online ecosystem in which companies constantly compete for users'
attention and use sophisticated user tracking and personalization methods to maximize their profit.
My research investigates the potential downsides of the algorithms commonly used by online platforms. Since these algorithms learn from human data, they are bound to recreate biases that are present in the real world. In this talk, I will first present a measurement methodology developed to monitor personalization algorithms in the context of platforms such as Google Search or online stores. Second, I will talk about recommendation and rating systems in the context of employment related platforms such as job search sites, freelancing marketplaces and online professional communities, and their danger to reinforce gender inequalities.