Kristina Lerman

TL;DR: This survey investigated different real-world applications that have shown biases in various ways, and created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems.

TL;DR: In this article, the authors present a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems and examine different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them.

TL;DR: In this paper, the authors analyzed data from two popular social news sites, Digg and Twitter, and tracked how interest in news stories spreads among them, and showed that social networks play a crucial role in the spread of information on these sites, and that network structure affects dynamics of information flow.

TL;DR: A statistical model is developed that quantifies the effect of the majority illusion and shows that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.

TL;DR: The COVID-19-TweetIDs GitHub repository as mentioned in this paper provides a multilingual COVID19 Twitter data set that is made available to the research community via a GitHub repository, with over 60% of the tweets in English.