Location
https://ccse.kennesaw.edu/computing-showcase/cday-programs/spring2021program.php
Event Website
http://journalistic-integrity.s3-website.us-east-2.amazonaws.com/
Document Type
Event
Start Date
26-4-2021 5:00 PM
Description
We are developing a web app to recognize and rate political bias in online journalism using artificial intelligence. All human writing inherently contains bias ,however bias is less harmful if it is transparent to the reader because they can now make informed decisions about what they read. We've collected articles and and reactions to them from online sources, and then used Neural Networks trained for natural language processing to determine bias. The project can predict bias labels on a news articles with 82% accuracy.Advisors(s): Reza Meimandi Parizi - course instructor Asher Nuckolls - project ownerTopic(s): Artificial IntelligenceSWE4724
Included in
UC-39 Journalistic Integrity vis Artifical Intelligence
https://ccse.kennesaw.edu/computing-showcase/cday-programs/spring2021program.php
We are developing a web app to recognize and rate political bias in online journalism using artificial intelligence. All human writing inherently contains bias ,however bias is less harmful if it is transparent to the reader because they can now make informed decisions about what they read. We've collected articles and and reactions to them from online sources, and then used Neural Networks trained for natural language processing to determine bias. The project can predict bias labels on a news articles with 82% accuracy.Advisors(s): Reza Meimandi Parizi - course instructor Asher Nuckolls - project ownerTopic(s): Artificial IntelligenceSWE4724
https://digitalcommons.kennesaw.edu/cday/spring/undergraduatecapstone/15