“Man pardoned after shooting police officer”
“Clinton Foundation Behind California Wildfire”
“Caravan of Immigrants at Border Thought to Include ISIS Members”
If you live in certain places in the United States, these are the kinds of article titles that pervade your Facebook News Feed. In the last three years, social networks have experienced an explosion in false articles – or “fake news.” Facebook’s ability to eliminate this content from its platform is not only imperative for its survival, but will fundamentally shape our social fabric, politics, and collective belief in truth. Machine learning is at the heart of this task.
Fake news exists for two reasons. The first (and most publicized) is political gain, whether by domestic special interest groups or foreign governments. And second, according to Facebook Product Manager Tessa Lyons, “much of the false news we see on Facebook is financially motivated” . Compensation for views gives content creators a powerful incentive to publicize click-bait in the form of fake news.
Because of the diversity, scale, and complexity of fake news, advanced Machine Learning techniques are necessary to identify it. In the last decade, “enormously increased data, significantly improved algorithms, and substantially more-powerful computer hardware” have ushered a renaissance in Machine Learning and Artificial Intelligence . New techniques like deep neural networks and reinforcement learning show incredible results in topics traditionally thought to be hard or impossible for computers. According to MIT Professor Erik Brynjolfsson, “Ninety percent of the digital data in the world today has been created in the past two years alone” . As the scale of social network data grows, monitoring cannot physically be done by humans. As described by Harvard Business School post-doctoral fellow Mike Yeomans, machine learning is a “branch of statistics, designed for a world of big data” – and sifting through the feeds of 2 billion Facebook users squarely fits this scope .
Facebook has already been highly successful in deploying machine learning in other places throughout its organization. For example, Facebook uses a tool called PhotoDNA to identify child pornography. Facebook’s algorithms have prompted over 1,000 calls to first responders after identifying users that may attempt suicide or harm themselves. And the natural language processing (NLP) system DeepText helped Facebook censor 2 million pieces of terrorist propaganda .
Looking forward, Facebook must employ similar techniques to address Fake News. In his testimonials before congress, CEO Mark Zuckerberg “references AI more than 30 times in explaining how the company would better police activity on its platform” . However today, their algorithms are still powered by an army of fact checkers “who manually mark fake stories” .
When public criticism of Facebook intensified last spring, the company began devoting significant resources to external initiatives. They ran ads during the NBA Playoffs admitting mistakes and produced a thought-provoking 12-minute documentary called Facing Facts – on the challenges of fighting misinformation . On the technical side, the company engaged Social Science One – a non-profit “partnership between academic researchers and private industry.” Through this collaboration, Facebook provides both funding and troves of anonymized internal data to academic researchers to pursue solutions to identifying fake news .
Sadly, Machine Learning isn’t just for the good-guys. A recent and exceptionally nefarious development is the use of machine learning to generate fake video (“deepfakes”). In this technique, a deep neural net (a common machine learning model) is fed hours of video of a person. Once trained, uses can produce video of that person doing or saying anything. In this way, machine learning can be used to create fake news.
The example video below shows a speech by Hillary Clinton, but delivered with President Donald Trump’s face and voice:
In July, Senator Mark R. Warner (Vice Chair of the Senate Intelligence Committee) published a white paper describing deepfakes as “poised to usher in an unprecedented wave of false or defamatory content” .
Machine learning can be used to create fake news, and it’s getting better, more difficult to detect, and easier to implement. Moreover, most people are unaware of deepfakes and would believe any video is a bonafide recording. Imagine deepfakes in a high profile election setting: millions of views would accrue before debunked, casting the legitimacy of the election into doubt . Perhaps the worst side-effect of deepfakes is the destructive impact to the credibility of real news and real recordings.
Machine learning and AI are increasingly being used by companies to improve their product and police their platforms. However, these same methods are also available to those with more perverse objectives. How can Facebook get ahead of the curve in identifying fake news generated by machine learning programs? When censoring, how can they ensure political objectivity when so much content comes with a right-wing bend? Finally, how should Facebook change its approach to user content, censoring, and monetization?
 Thompson, N. (2018). How Facebook Wants to Improve the Quality of Your News Feed. [online] WIRED. Available at: https://www.wired.com/story/how-facebook-wants-to-improve-the-quality-of-your-news-feed/ [Accessed 13 Nov. 2018].
 Brynjolfsson, E. and McAfee, A. (2017) ‘WHAT’S DRIVING THE MACHINE LEARNING EXPLOSION? Three factors make this AI’s moment’, Harvard Business Review Digital Articles, pp. 12–13. Available at: http://ezp-prod1.hul.harvard.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=124641872&site=ehost-live&scope=site (Accessed: 13 November 2018).
 Yeomans, M. (2015) ‘What Every Manager Should Know About Machine Learning’, Harvard Business Review Digital Articles, pp. 2–6. Available at: http://ezp-prod1.hul.harvard.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=118667151&site=ehost-live&scope=site (Accessed: 13 November 2018).
 Simonite, T. (2018). How Artificial Intelligence Can—and Can’t—Fix Facebook. [online] WIRED. Available at: https://www.wired.com/story/how-artificial-intelligence-canand-cantfix-facebook/ [Accessed 13 Nov. 2018].
 Neville, M. (2018). Facing Facts | Facebook Newsroom. [online] Newsroom.fb.com. Available at: https://newsroom.fb.com/news/2018/05/inside-feed-facing-facts-a-short-film/ [Accessed 13 Nov. 2018].
 Socialscience.one. (2018). Social Science One: Building Industry-Academic Partnerships. [online] Available at: https://socialscience.one/ [Accessed 13 Nov. 2018].
 Gilmer, D. (2018). A guide to ‘deepfakes,’ the internet’s latest moral crisis. [online] Mashable. Available at: https://mashable.com/2018/02/02/what-are-deepfakes/#ukmMTuyotqqb [Accessed 13 Nov. 2018].
 Warner, M. (2018). Potential Policy Proposals for Regulation of Social Media and Technology Firms. [online] Regmedia.co.uk. Available at: https://regmedia.co.uk/2018/07/30/warner_social_media_proposal.pdf [Accessed 13 Nov. 2018].
 Simonite, T. (2018). Will ‘Deepfakes’ Disrupt the Midterm Election?. [online] WIRED. Available at: https://www.wired.com/story/will-deepfakes-disrupt-the-midterm-election/ [Accessed 13 Nov. 2018].