Lesson 1: Disinformation
Overview
From deepfakes being used to harass women, widespread misinformation about coronavirus (labeled an "infodemic" by the WHO), fears about the role disinformation could play in the 2020 election, and news of extensive foreign influence operations, disinformation is in the news frequently and is an urgent issue. It is also indicative of the complexity and interdisciplinary nature of so many data ethics issues: disinformation involves tech design choices, bad actors, human psychology, misaligned financial incentives, and more.
Required Reading:
- Will Oremus, The Simplest Way to Spot Coronavirus Misinformation on Social Media: Effective approaches for identifying misinformation (similar to those used by professional fact-checers) run counter to the research techniques that many of us were taught in school. This outlines the work of Mike Caulfied on simple, yet often counter-intuitive approaches for users.
- Guillaume Chaslot, How Algorithms Can Learn to Discredit the Media: Chaslot is a former Google/YouTube engineer and founder of the non-profit watch group AlgoTransparency. He has done a lot to bring attention to issues with recommendation systems. For a counter view on the role of recommendation systems, see Rebecca Lewis’s work below.
- Rachelle Hampton, The Black Feminists Who Saw the Alt-Right Threat Coming: This provides some helpful history on a 2014 case of fraudulent accounts and coordinated manipulation, with techniques that would go on to be widely used in future disinformation campaigns.
- Renee DiResta, Mediating Consent: DiResta is a top expert on computational propaganda, who led one of the two teams that analyzed the dataset about Russian interference in the 2016 election for the Senate Intelligence Committee, and now works at the Stanford Internet Observatory
- Manuel Velasquez et al, “What is ethics?”: We will talk more foundations of ethics in week 3, after we’ve seen some case studies, but wanted to share this now.
Optional Reading:
Optional Lab for Coders:
Intro to Language Modeling & Text Generation: video lecture and jupyter notebook (from fast.ai NLP course)