EP05 - AI Journalism

Hello everyone and welcome to the Tech Journal. My name is Marc Vermeer Menom and I am the Digital speaker. Today I'm going to be looking at AI and journalism, the possibilities, limitations and what outcomes we can expect. So sit back and listen up and let's start speaking digital as I touched on in the first Tech Journal video. Check it out here if you haven't already. Microsoft made waves earlier this year when it announced that more than 70 journalists and editors across the US and the UK working on Microsoft's MSN website and its Edge browser had been let go. But their positions were not empty for long, though Microsoft quickly replaced them with GPT3, a state of the art language AI. The cutting edge digital employee quickly and accurately identifies relevant, interesting and most importantly, trending articles, performing the exact same job as the team it replaced, but cheaper and much more efficient. GPT3's talents don't just end with spotting patterns and trends though. This clever little thing can also work right, as he demonstrated in his guest article for the Guardian recently, Writing To Convince Humans that Robots Come in peace whilst AIs have come a long way, experts like Professor Christian Hammond predicted way back in 2012 that by 2027 more than 90% of news articles would be written by AI. And seeing as we are only seven years away, it isn't looking very likely. However, we are actually much closer than you would think, but but I'll touch upon that a little later. So the good news for us humans here is that AIs still don't do very well when left to their own devices. When it comes to writing opinion pieces commenting on a nuanced political situation, or articles exploring the human condition, diving deep into moral and ethical decisions, AIs are simply two processes short of a Macintosh. Although as we move forward with AIs now capable of doing more tasks with increased complexity, we we face several important what is the role of AI in journalism? What challenges will we see as a species and which challenges will we face? What do we need to really consider before bringing AI into the newsroom? So what is the current state of AI journalism? Well, machine written articles are more common than you'd expect. Over the past decade, Plug and Play articles have become an AI journalist favorite. This is because these articles are simple, they follow a formula and they do not need independent research. They cover simple topics like business and sports and don't require any much nuance. Bloomberg News uses an AI called Cyborg. It automatically scans companies quarterly reports and outputs an article with the most relevant information, while another AI called Wordsmith is known for writing about college basketball games. However, as soon as you leave the formulas, the cracks begin to appear. So for now, most topics are best left to journalists with actual fingers. Otherwise, while GPT3 can write several paragraphs of super convincing text, there is no guarantee the content will have any basis in reality. So while AIs don't do so well writing on their own, they do make pretty good assistants. One instance where AIs really excel here is when they are used to transcribe interviews. It's a basic job, just write out what someone says. But offloading this work onto an AI ends up saving journalists a huge amount of time, and while the results are rarely flawless, the few errors that are made are easily corrected by a human editor. We'll likely see a lot more of this in the future, as only a short while ago back in April 2019, Trint, an automated speech to text service, closed a $4.5 million funding round with the Associated Press and Google Digital News Innovation Fund amongst its investors. According to Jeff Kaufman, the co founder and former TV news reporter, the software can quickly output transcriptions with an accuracy rate of 95 to 99%. As I briefly touched on before, AIs can also assist in identifying trends and developments worthy of investigation. Back in 2018, we watched as Forbes rolled out a new AI content management system called Bertie. Bertie's job was to recommend articles, topics and headlines to contributors based on their previous work. In the months following Bertie joining the team, Forbes reported that the number of loyal visitors, visitors who visit the Forbes website more than once a month, had doubled. This then demonstrates that AI can really tap into what users want to see, leading to them filling in as curators, further spreading the AI's influence. And here's where we can thank Facebook. It is well known that Facebook's algorithm looks at user data and goes on to use this data when generating the user's feedback. Well, most news organizations followed Facebook's lead on this one and now use similar AI to keep track of all their subscribers data. The AI then analyzes their behavior and uses the data to personalize what the user has shown. This way, the more a news outlet knows about its customer, the more user specific content it can display on their website. Delivering a personalized experience, Encouraging interaction According to a 2019 survey by the digital media company Digiday, 70% of digital publishers like Microsoft, Bing, Associate Press Media and the New York Times personalize content for visitors. But AIs are not just helping out front, they are also helping in boardrooms making data driven business decisions. By collecting data and crunching the numbers, an AI can help managers and executives make decisions about a whole range of factors, including content decisions, subscriber acquisition, marketing campaign, and pricing. The Wall Street Journal's dynamic paywall is a good example of this. The paywall gives non members differing amounts of access based on the likelihood that they will buy a subscription. So, for example, a reader finding an article through the actual Wall Street Journals website will have more access to content before being blocked out by the paywall than someone who found the article through a link on social media. Basically an AI deciding who gets the free samples based on how likely the people are going to buy something. So far, so good AI seems pretty advanced. However, as Professor Christian Hammond's prediction showed earlier, AI journalism isn't where we thought it would be by now. According to a 2019 Journalism AI report, which surveyed 71 news organizations in 32 countries, there are still significant difficulties limiting AI. So what is limiting it and is there a hard limit? Well, the top three challenges of bringing AI into the newsroom seem to be financial resources, lack of knowledge or skills, and cultural resistance. Beyond these institutional barriers, there are other good reasons to question whether AI in journalism is really the game changer that advocates claim it will be. Columbia journalism professor Francesca Macroni estimates that in the future, only 8 to 12% of a reporter's task will will be replaceable by a machine. So things like interview transcription like I talked about earlier and maybe proofreading the state of AI for journalism helps illustrate the difference between strong and weak AI. Strong or general AI is a machine that approaches human level intelligence across the board. We still seem decades away from developing something like this, if it is even possible. While weak AI, on the other hand, is really good at a small number of specific tasks. This weak AI is what we are seeing pop up everywhere, and it is a lot more likely to find employment. At the end of the day, it's pretty reasonable to be skeptical that newsrooms will ever see an AI reporter that can stand shoulder to shoulder. Metaphorically, of course, with its human counterpart. Having said that, we cannot forget that AI is and will play an increasingly important support role in journalism. Whether it is creating article summaries, generating ideas and proposals, analyzing data and trends, finding interesting stories, or making marketing and subscriber decisions, it is pretty safe to say that weak AI will only become more prevalent with every passing year. Alright, so strong AI out, Weak AI in. Will there be any repercussions to this or is it pretty plain sailing? Well There seem to be varying repercussions of AI journalism, but most ideas seem to be focused around ethics. Ethics in journalism as well as AI is already a sensitive topic, and so combining journalism with AI makes for an especially contentious combination. After all, we are living through the era of fake News, where only 41% of Americans are reported to trust the media, while only 46% of Europeans say they trust their written pressure. This lack of trust is exactly why we are developing a platform like Maven. An ecosystem designed to enable AI and the crowd to score online content based on its reliability is so important. There are two distinct ethical concerns for mixing journalism and AI first, using AI to generate content, and second, using AI to curate and display content. Both issues play into each other too. Back in 2014, Facebook revealed the results of a controversial experiment with users news feeds. People who were exposed to positive emotional content on Facebook posted more positive posts of their own, while the inverse effect happened with people who saw more negative content. Similarly, a news company that promotes articles based only on users interactions would likely find itself in a race to the bottom, spitting out clickbait articles at news that induces sadness and outrage. Meaning an AI will probably decide to show more negative content since it gets more engagement. Meaning that it will produce more negative content, leading to users sharing more negative content, leading to more interactions, leading background to the AI generating more negative content, creating a self fulfilling prophecy or a self perpetuating cycle of negative news. To avoid this dystopian nightmare, news outlets must maintain a steady human hand on the wheel, as while AIs can churn out hundreds of cut and paste clickbait articles per day, they still need human editing and fact checking before they go to print. Likewise, humans need to oversee news creation algorithms to ensure that the results are consistently high quality and keep recommendation algorithms under control. Still, AI journalists have proven their ability to take on much of the field's hard labor collecting data, transcribing recordings, writing short interest articles, and so on. But when it comes to the work that truly makes a news organization stand out in depth, reporting political commentary and opinion columns, it is clear that humans will always be an essential part of the journalism industrial complex. And with that cheery note, I'll leave this video here. My name is Marc Vermenhom and I am the Digital Speaker. Don't forget to like and subscribe to keep up with all the latest digital news and I see you next time for your digital download. Stay Digital.