A new feature for Google Assistant, Duplex, stole the limelight at this year’s annual developer’s conference.[1] Duplex allows the app to book appointments for you by making phone calls on its own, speaking and sounding so life-like that it fools just about everyone into thinking it’s a real human.
The outcry was immediate. While some saw the feature’s usefulness, others found it frightening that computers could now fill the world with robot impostors.
It’s not hard to visualize how this technology could become a part of the fake news problem, but it’s only the latest: this is, after all, the age of Photoshop, Russian twitter bots, and phishing websites. As a Guardian article[2] points out, we now live in an era when audio and video tools such as Face2Face[3] can make public figures appear to say anything and act any way you wish. As the article puts it, ‘ We’ve long been told not to believe everything we read, but soon we’ll have to question everything we see and hear as well.’
However, the theme of this series of articles isn’t to add to the chorus of dystopian voices telling us we’re doomed to a world of fake news, but to discuss how to recognize and overcome misinformation and bias. Looking to the future, if AI and technology can be part of the problem, can they also be part of the cure?
Google certainly thinks so. It recently announced the Google News Initiative [4], an umbrella project designed to tie together all of the company’s existing projects to work with journalists, such as Google News Lab, First Draft, and the Digital News Initiative. The company will ante up $300 million over the next three years on its various journalism projects. Google’s interest in combating fake news is obvious; its core business is advertising; last year Google paid out $12.6 billion to publishers through advertising revenue splits. If users distrust and turn away from the sites the search engine take them too, both Google and its partners suffer.
From the 20,000 foot level, Google’s efforts appear to largely be centered on steering viewers to ‘more authoritative sources’, and to educating both journalists and viewers. For example, MediaWise [5], a joint project with Poynter Institute, Stanford University, and the Local Media Association, will help middle and high school students become smarter consumers of news.
Google’s education efforts aim to steer news consumers toward what Google calls ‘more authoritative content.’ For example, YouTube users now can have a ‘Top News’ shelf which contains, in Google’s words, ‘highlighted relevant content from verified news sources.’
While this is a laudable goal, I’m a bit of a skeptic. As a previous article [tk link to Trust No One article] points out, a news story can be completely truthful but still try to influence your views and push the author’s (or his organization’s) opinion or agenda- in other words, be biased or slanted. These days this is all too often the case for both alternative and mainstream media. It’s therefore up to you, the reader or viewer, to decide what distinguishes true from false, and belief from opinion. The good news is that software is increasingly able to help with this process.
For instance, Google’s Jigsaw[6] is an internal incubator that ‘builds technology to tackle a range of global security challenges ranging from thwarting online censorship to mitigating the threats from digital attacks to countering violent extremism to protecting people from online harassment.’ The projects created, if successful, graduate to wider use in various ways. One example is Perspective[7], an API which uses machine learning models to score the perceived impact of a comment, to provide realtime feedback to help moderators do their job better. Another is Share the Facts[8], developed jointly with Duke University, which allows readers to share fact-check information either by querying or by embedding a link in an article or blog post.
Meanwhile, Facebook has revamped its news feed by putting more emphasis on interpersonal interactions from friends and family than on news sources. Twitter sent an email to 678,000 users informing them they may have received posts from a now-suspended Russian propaganda outfit called the Intenet Research Agency. According to one paper[9], bots make it easy to generate high volumes of low quality or inaccurate information.
These actions are unlikely to end the social media giants’ controversies. Facebook argues that it’s not a news company in the traditional sense, but as long as it distributes other publishers’ content, it’s an editor. As for Twitter, estimates suggest that as many as 48 million accounts- fifteen percent of all accounts- are actually robots- mostly benign, scheduling posts for timing, providing news alerts or customer service- but that’s still a lot of bots, and as the beginning of this article suggests, the ability of bots can only increase. Even without bots, its users- you and I- have our biases, and they’re going to bump into other peoples’ biases on social media platforms.
The media giants aren’t the only groups confronting the fake news problem. Advances in machine learning and natural language programming now make it possible to develop systems that can examine news articles for factual truth and biases, and anyone can try. For example, there’s Logically[12], an intelligent news feed that ‘uses complex analytics to identify fake news, separate fact from falsehoods, and illustrate discrepancies in the sentiment of journalism from across the political spectrum.’
What’s currently possible is automatically scraping news transcripts to find claims that can be tested as true or false, then matching them against libraries of existing fact checks like Share the Facts[14], one of the aforementioned Jigsaw projects. Examples of such software are Full Fact[15], The Duke Reporter’s Lab tool[16], and Checkueado[17].
Finding assertions to fact-check is half the problem; the other half is checking them. According to a Reuters report[13], fully automated fact-checking isn’t even close to being capable of the judgment that journalists apply on a day-to-day basis. The process of fact-checking is labor-intensive; few asserted facts are checked compared to the number of assertions made, despite an increased number of fact-checker sites in recent years.
One way to get around this is to use crowdsourced volunteer fact-checkers. Perhaps the most interesting attempt at this approach is Wikitribune, created by Wikipedia founder Jimmy Wales, which plans to hire journalists and pair them with volunteers. How well this will work (and scale) remains to be seen, but if, as Wales hopes, Wikitribute can do as a news service what Wikipedia accomplished as an encyclopedia, it’s worth watching.
A paper by Chloe Lim, a Ph.D. student at Stanford University, reports little overlap in the statements that fact-checkers check[18]. Out of 1065 fact-checks by PolitiFact and 240 fact-checks by The Washington Post’s Fact-Checker, there were only 70 statements that both fact-checkers checked. The study found that the fact-checkers gave consistent ratings for 56 out of 70 statements, which means that one out every five times, the two fact-checkers disagree on the accuracy of statements. One reason cited by Lim is that politicians’ statements are often vague or general, and thus subject to interpretation.
Meanwhile, there are other attempts. There’s even a competition, Fake News Challenge[11], a grassroots effort of over 100 volunteers and 71 teams from academia and industry around the world whose goal is ‘to address the problem of fake news by organizing a competition to foster development of tools to help human fact-checkers identify hoaxes and deliberate misinformation in news stories’.
As a previous article[19] points out, bias takes many forms. Fact-checkers won’t catch all bias and may contain biases of its own, such as which facts to check and which to ignore. Other substantial issues include opinion vs. Objectivity in reporting, how sources are identified and used, and linguistic manipulation such as slanted language, exaggeration, over-generalization, and appeals to emotion.
[1]
https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html
[2] https://www.theguardian.com/technology/2017/jul/26/fake-news-obama-video-trump-face2face-doctored-content
[3] https://web.stanford.edu/~zollhoef/papers/CVPR2016_Face2Face/paper.pdf
[4]
https://blog.google/topics/google-news-initiative/announcing-google-news-initiative/
[5]
https://ed.stanford.edu/news/stanford-education-scholars-create-resources-help-young-people-spot-fake-information-online
[6] Google Jigsaw
https://jigsaw.google.com/
[7] Perspective
https://www.perspectiveapi.com/#/
[8] Share the Facts
https://www.sharethefacts.org/
[9] Measuring Online Social Bubble
https://arxiv.org/abs/1502.07162
[10] Google Cloud Language
https://cloud.google.com/natural-language/
[11] Fake News Challenge http://www.fakenewschallenge.org/
[12] Logically
https://logically.co.uk
[13] Factsheet: Understanding the Promise and Limits of Automated Fact-Checking
https://reutersinstitute.politics.ox.ac.uk/risj-review/factsheet-understanding-promise-and-limits-automated-fact-checking
[14] Share the Facts
http://www.sharethefacts.org/
[15] Full Fact
https://fullfact.org/
[16] Duke Reporter’s Lab
https://reporterslab.org/
[17] Chequeado
http://chequeado.com/
[18] Checking How Fact-checkers Check
https://drive.google.com/file/d/0B_wUaJ01JSddZTNWVWpkRzVXUzg/view
[19] Recognizing Bias
(my article)
[20] FakeBox
https://towardsdatascience.com/i-trained-fake-news-detection-ai-with-95-accuracy-and-almost-went-crazy-d10589aa57c
https://www.nbcnews.com/mach/science/fake-news-still-problem-ai-solution-ncna848276
https://www.technologyreview.com/s/609717/can-ai-win-the-war-against-fake-news/
https://channels.theinnovationenterprise.com/articles/how-can-artificial-intelligence-combat-fake-news
https://www.theverge.com/2018/4/5/17202886/facebook-fake-news-moderation-ai-challenges
http://www.bqlive.co.uk/creative-media/2017/09/29/news/new-start-up-aims-to-combat-fake-news-27967/
(note that https://thelogically.com, Jain’s site, is down)
http://bigdata-madesimple.com/could-ai-help-stop-fake-news-in-the-near-future/
http://www.fakenewschallenge.org/
https://www.facebook.com/verge/videos/1618100861559584/
https://www.forbes.com/sites/alanwolk/2018/01/17/can-enterras-advanced-ai-systems-stop-the-fake-news-epidemic/#4daa3cc204
https://www.entrupy.com/technology/
https://en.wikipedia.org/wiki/Sentiment_analysis
https://www.prnewswire.com/news-releases/media-industry-titans-to-launch-vidl-a-transformational-news-technology-platform-utilizing-ai-technology-and-blockchain-to-report-accurate-global-news-events-and-incidents-in-an-automated-real-time-manner-300579694.html
https://www.vidlnewscorp.com/
https://web.stanford.edu/class/cs224n/reports/2710385.pdf
http://graphics.wsj.com/blue-feed-red-feed/
https://venturebeat.com/2017/06/11/how-ai-is-winning-the-war-against-fake-news/
Social media orchestration & bots
Mozilla web literacy course
Education vs. Brainwashing
Fact checking will be expected; if it’s not checked it’s not true
Can software do it if we can’t?