2020 Workshop on NLP4IF: Censorship, Disinformation, and Propaganda

Keynote Speakers

Aylin Caliskan

(George Washington University)

aylin@gwu.edu

Bio: Aylin Caliskan is an Assistant Professor of Computer Science at George Washington University. Her research interests lie in AI ethics, bias in AI, machine learning, and the implications of machine intelligence on fairness and privacy. She investigates the reasoning behind biased AI representations and decisions by developing explainability methods that uncover and quantify biases of machines. Building these transparency enhancing algorithms involves the heavy use of machine learning, natural language processing, and computer vision in novel ways to interpret AI and gain insights about bias in machines as well as society. In her recent publication in Science, she demonstrated how semantics derived from language corpora contain human-like biases. Prior to that, she developed novel privacy attacks to de-anonymize programmers using code stylometry. Her presentations on both de-anonymization and bias in machine learning are the recipients of best talk awards. Her work on semi-automated anonymization of writing style furthermore received the Privacy Enhancing Technologies Symposium Best Paper Award. Aylin holds a PhD in Computer Science from Drexel University and a Master of Science in Robotics from the University of Pennsylvania. Before joining the faculty at George Washington University, she was a Postdoctoral Researcher and a Fellow at Princeton University's Center for Information Technology Policy.

Title: Implications of Biased AI on Democracy, Equity, and Justice

Abstract: Billions of people on the internet are exposed to the outputs of downstream natural language processing (NLP) applications on a daily basis. Many of these NLP applications use word embeddings as general purpose language representations. In this talk, Aylin Caliskan will introduce the Word Embedding Association Test (WEAT) to demonstrate that word embeddings trained on language corpora embed the biases and associations documented by the Implicit Association Test in social psychology. In particular, WEAT measures how statistical regularities of language capture biases and stereotypes, such as racism, sexism, and attitudes toward social groups. Word embeddings are trained on text collected from the internet, that includes society's organic natural language data in addition to text from information influence operations. The adaptation of WEAT to the information influence domain automatically characterizes overall attitudes and biases associated with emerging information influence operations. Accurate analysis of these emerging topics usually requires laborious, manual analysis by experts to annotate a large set of data points to identify biases in new topics. We validate our practical and non-parametric method using known information operation-related tweets from Twitter's Transparency Report. We perform a case study on the COVID-19 pandemic to evaluate our method's performance on non-labeled Twitter data, demonstrating its usability in emerging domains.

Stephan Lewandowsky

(University of Bristol)

stephan.lewandowsky@bristol.ac.uk

@STWorg

Bio: Professor Stephan Lewandowsky is a cognitive scientist at the University of Bristol. He was an Australian Professorial Fellow from 2007 to 2012, and was awarded a Discovery Outstanding Researcher Award from the Australian Research Council in 2011.His research examines people’s memory, decision making, and knowledge structures, with a particular emphasis on how people update information in memory. His most recent research interests examine the potential conflict between human cognition and the physics of the global climate, which has led him into research in climate science and climate modeling.He has published more than 200 scholarly articles, chapters, and books, including numerous papers on how people respond to corrections of misinformation and what variables determine people’s acceptance of scientific findings.Professor Lewandowsky is an award-winning teacher and was Associate Editor of the Journal of Experimental Psychology: Learning, Memory, and Cognition from 2006-2008. He has also contributed around 50 opinion pieces to the global media on issues related to climate change “skepticism” and the coverage of science in the media. He is currently serving as Digital Content Editor for the Psychonomic Society and blogs routinely on cognitive research.

Title: Diversionary agenda setting and micro-targeted persuasion

Abstract: We are said to live in a “post-truth” era in which “fake news” has replaced real information, denial has compromised science, and the ontology of knowledge and truth has taken on a relativist element. I argue that to defend evidence-based reasoning and knowledge against those attacks, we must understand the strategies by which the post-truth world is driven forward. I depart from the premise that the post-truth era did not arise spontaneously but is the result of a highly effective political movement that deploys a large number of rhetorical strategies. I focus on two strategies: Diversionary agenda-setting by social media and the use of “micro-targeting” of political messages online. I present evidence for the existence of each strategy and its impact, and how it might be countered.