Title | New Faces of Bias in Online

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 present examples of the diverse methodologies our group in Zurich uses to address questions around the impact of technology on society and individuals, and how we can learn from these studies to create fairer and safer online environments.

Bio | Anikó is an Associate Professor at the computer science department of the University of Zürich. She received her PhD from the College of Computer & Information Science at Northeastern University advised by Alan Mislove and David Lazer. Broadly, her work investigates a variety of content serving websites such as Search Engines, Online Stores, Job Search Sites or Freelance Marketplaces. In this quickly changing online ecosystem companies track users' every move and feed the collected data into big data algorithms in order to match them with the most interesting, most relevant content. Since these algorithms learn on human data they are likely to pick up on social biases and unintentionally reinforce them. In her PhD work, Anikó created a methodology called Algorithmic Auditing which tries to uncover the potential negative impacts of large online systems. Examples of such audits include examining the "Filter Bubble effect" on Google Search, online price discrimination or detecting inequalities in online labor markets.