Nicholas Diakopoulos grew up being exposed to journalism, as his father was a journalist. The younger Dikopoulos decided he wanted to study computer science, and completed a Ph.D. in the field at the Georgia Institute of Technology (Georgia Tech). Midway through his doctorate, he started to think about combining journalism and computation into a new field: computational journalism.
Today, Diakopoulos is an assistant professor in communication studies and computer science at Northwestern University; he also serves as director of the university's Computational Journalism Lab.
In his new book Automating the News: How Algorithms Are Rewriting the Media, Diakopoulos explores the new field of computational journalism, which he has been helping to establish since 2007. The book makes clear how algorithms are changing the journalistic production pipeline from information gathering to sense-making, story-telling, and finally news distribution. Artificial intelligence (AI) already is used to personalize article recommendations, summarize articles, mine data in documents, transcribe recorded interviews, automate content production, moderate comments, and to eliminate (but unfortunately, also to produce) fake news.
What should all journalists know about your book?
A lot of journalists who don't understand how artificial intelligence works might feel threatened: 'oh, AI bots are going to write all our stories. We will be out of work'. In my book, I show over and over again that the cognitive labor of journalists is very difficult to completely automate. There are, of course, bits and pieces that can and will be automated, but more important will be the hybridization of AI with journalists. Jobs in journalism will not disappear, but instead will change.
Can you give an example of what AI can do for journalism that would be impossible without it?
One of the most compelling and important scenarios for AI in journalism is in using data mining to help discover new stories. I really like the example of how the Atlanta Journal Constitution discovered the misconduct of medical doctors by using machine learning to sift through 100,000 documents. In 2016, the newspaper published an investigative report uncovering more than 2,400 doctors across the U.S. who had been disciplined for sexual misconduct in their practice; about half of them still had licenses and were still seeing patients. It would have taken journalists thousands of hours to read all 100,000 documents. Machine learning selected only those documents with the highest chance of containing information about misconduct. Thanks to this, the job became doable for journalists. This is a great example of a story that journalists wouldn't find, at least not at that scale, if they didn't have computational techniques.
You write in your book that classical news organizations need to be more like Google. Why is that?
At their core, both computing and journalism share a focus on transforming and adding value to information; so, in a way, Google is in the same business as most news organizations. They organize information and knowledge. If news organizations want to compete as information and knowledge producers, they need to be a bit more like Google. We already see that big information companies like Thompson Reuters, Bloomberg, The New York Times, The Washington Post, and BBC get it; they are already deploying a fair bit of AI and automation. A big open question is what will happen to local news media; they are at a disadvantage in terms of resources and their ability to develop new AI tools.
How do we find the right way to hybridize between journalists and AI tools?
It's all about training and education. We need to engender computational thinking and data-thinking in journalists. Let's develop degrees in computational journalism. We also need to work on the transfer of domain expertise between journalists and computationalists. To achieve this, we could introduce computationalists in the newsroom and let them explore which tasks can be automated. Or the other way around, put journalists and editors in a computational environment, allowing them to interact and collaborate in computational work.
What will the new kind of work for journalists look like?
In the hybridization of workflows, more often than not AI technologies actually create new types of work related to things like configuring, updating, tweaking, validating, and generally maintaining and supervising systems. This might include tasks like making sure input data streams are updated, editing knowledge bases or metadata, or tweaking any of the rule-sets built into content templates. There will be more work created, but it will be different from traditional editorial work.
In your book, you are optimistic about the hybridization of AI and journalism. Can you explain your optimism?
Technology influences us, but we also shape technology. Journalism and technology will co-evolve. I reject the idea of technological determinism, the idea that technology has its own will and humans can only follow. Part of my goal with the book is to empower journalists to see themselves as designers of the future of algorithmic news media. AI is a new medium and journalists will need to learn to express and exercise their ethical and normative journalistic values through the AI systems that they implement.
Bennie Mols is a science and technology writer based in Amsterdam, the Netherlands.
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