Racist, Sexist, and Incoherent: The poor quality of comp...  — Josh Eisenberg
Channel: IgniteNYC
Date: 11/06/2018

Josh Eisenberg
“Racist, Sexist, and Incoherent – The poor quality of computationally generated language”

Josh researches the computational understanding of narrative and stories. He is a computer science Ph.D. candidate in the Cognac Laboratory at Florida International University in Miami. He has a patents pending for the computational classification of narrative point of view, diegesis, and for automatic extraction of stories in text. Josh enjoys traveling, food, and open air music festivals. 

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When I tell people I teach computers how to understand stories, they always show me a text automatically generated by a computer-- and it tends to be garbage, totally incoherent. How could a computer write a good story if it doesn’t understand what a story is, who the narrator is, or the concepts of plot, irony, and character development? They can't! They must understand narrative before they are expected to generate it. 

A second problem with computationally generated language is that they are often biased against minority groups of people. Microsoft's Twitter chat bot Tae tweeted racist language against black people and Jews, and sexist language against women (source: https://gizmodo.com/here-are-the-microsoft-twitter-bot-s-craziest-racist-ra-1766820160). Google's sentiment analysis program was found to yield more negative sentiment for almost every group of people other than straight white men (source: https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias).

Natural language generation has problems generating tolerant and coherent content. This is a huge problem. In this talk I will briefly cover the problems of natural language generation, and how we can begin to fix them, and make computers more coherent, and more tolerant to people of diverse backgrounds and identities.