Estimates say that today’s AI can automate only about 15 percent of a reporter’s job and 9 percent of an editor’s job. But that doesn’t mean AI won’t change a lot of the work that remains.
“Artificial neural networks are advancing rapidly in their ability to synthesize content — including images, videos, and texts — that are increasingly indistinguishable from authentic content.”
At Georgia Tech’s Computation + Journalism Symposium, representatives from both fields explored what the vibrant news information environment might look like.
As algorithms play an ever-larger role in how we get news and information, it’s important to realize the ways that bias — intentional or not — can seep into their decisions.
What’s the best way to follow how the news is changing?
Our daily email, with all the freshest future-of-journalism news.
Diakopoulos, Nicholas. "Nick Diakopoulos: Understanding bias in computational news media." Nieman Journalism Lab. Nieman Foundation for Journalism at Harvard, 10 Dec. 2012. Web. 17 Jun. 2019.
APA
Diakopoulos, N. (2012, Dec. 10). Nick Diakopoulos: Understanding bias in computational news media. Nieman Journalism Lab. Retrieved June 17, 2019, from https://www.niemanlab.org/2012/12/nick-diakopoulos-understanding-bias-in-computational-news-media/
Chicago
Diakopoulos, Nicholas. "Nick Diakopoulos: Understanding bias in computational news media." Nieman Journalism Lab. Last modified December 10, 2012. Accessed June 17, 2019. https://www.niemanlab.org/2012/12/nick-diakopoulos-understanding-bias-in-computational-news-media/.
Wikipedia
{{cite web
| url = https://www.niemanlab.org/2012/12/nick-diakopoulos-understanding-bias-in-computational-news-media/
| title = Nick Diakopoulos: Understanding bias in computational news media
| last = Diakopoulos
| first = Nicholas
| work = [[Nieman Journalism Lab]]
| date = 10 December 2012
| accessdate = 17 June 2019
| ref = {{harvid|Diakopoulos|2012}}
}}
