Digital Methods Winter School and Data Sprint 2021: Bias in content recommendation and moderation

Description

Digital Methods Winter School and Data Sprint

The Digital Methods Initiative (DMI), Amsterdam, is holding its annual Winter School on 'Bias in content recommendation and moderation'. The format is that of a (social media and web) data sprint, with tutorials as well as hands-on work for telling stories with data. There is also a programme of keynote speakers. It is intended for advanced Master's students, PhD candidates and motivated scholars who would like to work on (and complete) a digital methods project in an intensive workshop setting. For a preview of what the event is like, you can view short video clips from previous editions of the School.

When one argues that algorithms are biased, how is the case made? Similarly, when it is claimed that content moderation (that results in removal or demonetisation) is unfair, what is considered to be a convincing account? This year’s Digital Methods Winter School is dedicated to the empirical study of the bias in algorithmic output and moderated content. We would like to take stock of the claims made about biases (in search engines as well as social media platforms), and pay special attention to the measurement and representation of these biases. First, we would like to present the celebrated cases (such as discriminatory pricing, stereotyping and computer vision training), but also a discussion of the supposed political motivations surrounding takedowns by Google, Facebook, Twitter and others. Subsequently, we would like to critique and learn from how bias is represented and also put forward methods that capture and seek to lay bare bias, such as algorithmic auditing and other testing regimes. Finally, we would like to visualise these biases in ways that make them more legible and apparent.

Period	4 Jan 2021 → 8 Jan 2021
Event type	Workshop
Location	Amsterdam, Netherlands
Degree of Recognition	International