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BBC partnered with Google and Facebook on general election fake news

Edmund Heaphy

• The BBC partnered with Google (GOOGL), Microsoft (MSFT), Facebook (FB), and other news outlets to stymie the spread of fake news during December’s UK general election, its director-general Tony Hall said on Thursday.

Hall said that the BBC had been working “collaboratively” with the Wall Street Journal, the Financial Times, India’s Hindu newspaper on a partnership with Microsoft and Google to identify misinformation, as part of a previously announced initiative.

But Hall said on Thursday for the first time that it had been used prior to last month’s UK vote.

The BBC in September announced that it was working with the technology companies to develop an early warning system to use during elections or when lives may be at risk, calling the move a “crucial” step to fight disinformation.

The plan also includes media education, voter information plans, and shared learning initiatives.

The initiative works to de-emphasise stories that are just “plain wrong,” Hall said, speaking during a panel discussion at the World Economic Forum in Davos.

“We’ve tried this out on paper exercises, but we tried it for real … in the UK election, and it worked. That combination and contact between media that people trust and Google, Facebook, and whatever. It worked. And we took down some stuff which was just plain wrong — in copyright terms, but just wrong.”

“By the way, we haven’t talked about this anywhere yet, but why not here?”

Neither the BBC nor Google immediately responded to a request for more information about how the initiative was used in the general election.

Earlier in the talk, Hall noted that the BBC was still the most trusted source of news in the UK.

“For us, for the BBC, people still trust us more than any other form of media in the UK. Globally, trust is very very high.”

“And why do people use us? They may use three, four, five sources of news each day, but they come to us because they want to check whether [a story] is right,” he said.

Source: https://in.news.yahoo.com/bbc-partnered-with-google-microsoft-facebook-on-general-election-fake-news-word-economic-forum-in-davos-162351972.html

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Building a Lie Detector for Images

• A new paper from UC Berkeley and Adobe researchers declares war on fake images
• Leveraging a custom dataset and fresh evaluation metric, the research team introduces a general image forensics approach that achieves high average precision in the detection of CNN-generated imagery

By: Synced

The Internet is full of fun fake images — from flying sharks and cows on cars to a dizzying variety of celebrity mashups. Hyperrealistic image and video fakes generated by convolutional neural networks (CNNs) however are no laughing matter — in fact they can be downright dangerous. Deepfake porn reared its ugly head in 2018, fake political speeches by world leaders have cast doubt on news sources, and during the recent Australian bushfires manipulated images mislead people regarding the location and size of fires. Fake images and videos are giving AI a black eye — but how can the machine learning community fight back?

A new paper from UC Berkeley and Adobe researchers declares war on fake images. Leveraging a custom dataset and fresh evaluation metric, the research team introduces a general image forensics approach that achieves high average precision in the detection of CNN-generated imagery

Spotting such generated images may seem to be a relatively simple task — just train a classifier using fake images versus real images. In fact, the challenge is far more complicated for a number of reasons. Fake images would likely be generated from different datasets, which would incorporate different dataset biases. Fake features are more difficult to detect when the training dataset of the model differs from the dataset used to generate the fake image. Also, network architectures and loss functions can quickly evolve beyond the abilities of a fake image detection model. Finally, images may be pre-processed or post-processed, which increases the difficulty in identifying common features across a set of fake images.

To address these and other issues, the researchers built a dataset of CNN-based generation models spanning a variety of architectures, datasets and loss functions. Real images were then pre-processed and an equal number of fake images generated from each model — from GANs to deepfakes. Due to its high variety, the resulting dataset minimizes biases from either training datasets or model architectures.

The fake image detection model was built on ProGAN, an unconditional GAN model for random image generation with simple CNN based structure, and trained on the new dataset. Evaluated on various CNN image generating methods, the model’s average precision was significantly higher than the control groups.

Data augmentation is another approach the researchers used to improve detection of fake images that had been post-processed after generation. The training images (fake/real) underwent several additional augmentation variants, from Gaussian blur to JPEG compression. Researchers found that including data augmentation in the training set significantly increased model robustness, especially when dealing with post-processed images.

Researchers find the “fingerprint” of CNN-generated images.

The researchers note however that even the best detector will still have trade-offs between true detection and false-positive rates, and it is very likely a malicious user could simply handpick a simple fake image that passes the detection threshold. Another concern is that the post-processing effects added to fake images may increase detection difficulty, since the fake image fingerprints might be distorted during the post-processing. There are also many fake images that were not generated but rather photoshopped, and the detector won’t work on images produced through such shallow methods

The new study does a fine job of identifying the fingerprint of images doctored with various CNN-based image synthesis methods. The researchers however caution that this is one battle — the war on fake images has only just begun.

Source: https://syncedreview.com/2020/01/15/building-a-lie-detector-for-images/

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“Rated false”: Here’s the most interesting new research on fake news and fact-checking

• What better way to start the new year than by learning new things about how best to battle fake news and other forms of online misinformation?
• Below is a sampling of the research published in 2019 — seven journal articles that examine fake news from multiple angles, including what makes fact-checking most effective and the potential use of crowdsourcing to help detect false content on social media.

By Denise-Marie Ordway

Our friends at Journalist’s Resource, that’s who. JR is a project of the Shorenstein Center on Media, Politics and Public Policy at the Harvard Kennedy School, and they spend their time examining the new academic literature in media, social science, and other fields, summarizing the high points and giving you a point of entry.

Here, JR’s managing editor, Denise-Marie Ordway, sums up some of the most compelling papers on fake news and fact-checking published in 2019. (You can also read some of her other roundups focusing on research from 2018 and 2017.)

What better way to start the new year than by learning new things about how best to battle fake news and other forms of online misinformation? Below is a sampling of the research published in 2019 — seven journal articles that examine fake news from multiple angles, including what makes fact-checking most effective and the potential use of crowdsourcing to help detect false content on social media.

Because getting good news is also a great way to start 2020, I included a study that suggests President Donald Trump’s “fake news” tweets aimed at discrediting news coverage could actually help journalists. The authors of that paper recommend journalists “engage in a sort of news jujitsu, turning the negative energy of Trump’s tweets into a force for creating additional interest in news.” 

“Real solutions for fake news? Measuring the effectiveness of general warnings and fact-check tags in reducing belief in false stories on social media”: From Dartmouth College and the University of Michigan, published in Political Behavior. By Katherine Clayton, Spencer Blair, Jonathan A. Busam, Samuel Forstner, John Glance, Guy Green, Anna Kawata, Akhila Kovvuri, Jonathan Martin, Evan Morgan, Morgan Sandhu, Rachel Sang, Rachel Scholz‑Bright, Austin T. Welch, Andrew G. Wolff, Amanda Zhou, and Brendan Nyhan.

This study provides several new insights about the most effective ways to counter fake news on social media. Researchers found that when fake news headlines were flagged with a tag that says “Rated false,” people were less likely to accept the headline as accurate than when headlines carried a “Disputed” tag. They also found that posting a general warning telling readers to beware of misleading content could backfire. After seeing a general warning, study participants were less likely to believe true headlines and false ones.

The authors note that while their sample of 2,994 U.S. adults isn’t nationally representative, the feedback they got demonstrates that online fake news can be countered “with some degree of success.” “The findings suggest that the specific warnings were more effective because they reduced belief solely for false headlines and did not create spillover effects on perceived accuracy of true news,” they write.

“Fighting misinformation on social media using crowdsourced judgments of news source quality”: From the University of Regina and Massachusetts Institute of Technology, published in the Proceedings of the National Academy of Sciences. By Gordon Pennycook and David G. Rand.

It would be time-consuming and expensive to hire crowds of professional fact-checkers to find and flag all the false content on social media. But what if the laypeople who use those platforms pitched in? Could they accurately assess the trustworthiness of news websites, even if prior research indicates they don’t do a good job judging the reliability of individual news articles? This research article, which examines the results of two related experiments with almost 2,000 participants, finds the idea has promise.

“We find remarkably high agreement between fact-checkers and laypeople,” the authors write. “This agreement is largely driven by both laypeople and fact-checkers giving very low ratings to hyper-partisan and fake news sites.”

The authors note that in order to accurately assess sites, however, people need to be familiar with them. When news sites are new or unfamiliar, they’re likely to be rated as unreliable, the authors explain. Their analysis also finds that Democrats were better at gauging the trustworthiness of media organizations than Republicans — their ratings were more similar to those of professional fact checkers. Republicans were more distrusting of mainstream news organizations. 

“All the president’s tweets: Effects of exposure to Trump’s ‘fake news’ accusations on perceptions of journalists, news stories, and issue evaluation”: From Virginia Tech and EAB, published in Mass Communication and Society. By Daniel J. Tamul, Adrienne Holz Ivory, Jessica Hotter, and Jordan Wolf. 

When Trump turns to Twitter to accuse legitimate news outlets of being “fake news,” does the public’s view of journalists change? Are people who read his tweets less likely to believe news coverage? To investigate such questions, researchers conducted two studies, during which they showed some participants a sampling of the president’s “fake news” tweets and asked them to read a news story. 

Here’s what the researchers learned: The more tweets people chose to read, the greater their intent to read more news in the future. As participants read more tweets, their assessments of news stories’ and journalists’ credibility also rose. “If anything, we can conclude that Trump’s tweets about fake news drive greater interest in news more generally,” the authors write. 

The authors’ findings, however, cannot be generalized beyond the individuals who participated in the two studies — 331 people for the first study and then 1,588 for the second, more than half of whom were undergraduate students. 

Based on their findings, the researchers offer a few suggestions for journalists. “In the short term,” they write, “if journalists can push out stories to social media feeds immediately after Trump or others tweet about legitimate news as being ‘fake news,’ then practitioners may disarm Trump’s toxic rhetoric and even enhance the perceived credibility of and demand for their own work. Using hashtags, quickly posting stories in response to Trump, and replying directly to him may also tether news accounts to the tweets in social media feeds.” 

“Who shared it?: Deciding what news to trust on social media”: From NORC at the University of Chicago and the American Press Institute, published in Digital Journalism. By David Sterrett, Dan Malato, Jennifer Benz, Liz Kantor, Trevor Tompson, Tom Rosenstiel, Jeff Sonderman, and Kevin Loker.

This study looks at whether news outlets or public figures have a greater influence on people’s perception of a news article’s trustworthiness. The findings suggest that when a public figure such as Oprah Winfrey or Dr. Oz shares a news article on social media, people’s attitude toward the article is linked to how much they trust the public figure. A news outlet’s reputation appears to have far less impact. 

In fact, researchers found mixed evidence that audiences will be more likely to trust and engage with news if it comes from a reputable news outlet than if it comes from a fake news website. The authors write that “if people do not know a [news outlet] source, they approach its information similarly to how they would a [news outlet] source they know and trust.” 

The authors note that the conditions under which they conducted the study were somewhat different from those that participants would likely encounter in real life. Researchers asked a nationally representative sample of 1,489 adults to read and answer questions about a simulated Facebook post that focused on a news article, which appeared to have been shared by one of eight public figures. In real life, these adults might have responded differently had they spotted such a post on their personal Facebook feeds, the authors explain. 

Still, the findings provide new insights on how people interpret and engage with news. “For news organizations who often rely on the strength of their brands to maintain trust in their audience, this study suggests that how people perceive their reporting on social media may have little to do with that brand,” the authors write. “A greater presence or role for individual journalists on social networks may help them boost trust in the content they create and share.” 

“Trends in the diffusion of misinformation on social media”: From New York University and Stanford University, published in Research and Politics. By Hunt Allcott, Matthew Gentzkow, and Chuan Yu.

This paper looks at changes in the volume of misinformation circulating on social media. The gist: Since 2016, interactions with false content on Facebook have dropped dramatically but have risen on Twitter. Still, lots of people continue to click on, comment on, like and share misinformation.

The researchers looked at how often the public interacted with stories from 569 fake news websites that appeared on Facebook and Twitter between January 2015 and July 2018. They found that Facebook engagements fell from about 160 million a month in late 2016 to about 60 million a month in mid-2018. On Twitter, material from fake news sites was shared about 4 million times a month in late 2016 and grew to about 5 million shares a month in mid-2018.

The authors write that the evidence is “consistent with the view that the overall magnitude of the misinformation problem may have declined, possibly due to changes to the Facebook platform following the 2016 election.” 

“Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning”: From Yale University, published in Cognition. By Gordon Pennycook and David G. Rand. 

This study looks at the cognitive mechanisms behind belief in fake news by investigating whether fake news has gained traction because of political partisanship or because some people lack strong reasoning skills. A key finding: Adults who performed better on a cognitive test were better able to detect fake news, regardless of their political affiliation or education levels and whether the headlines they read were pro-Democrat, pro-Republican or politically neutral. Across two studies conducted with 3,446 participants, the evidence suggests that “susceptibility to fake news is driven more by lazy thinking than it is by partisan bias per se,” the authors write. 

The authors also discovered that study participants who supported Trump had a weaker capacity for differentiating between real and fake news than did those who supported 2016 presidential candidate Hillary Clinton. The authors write that they are not sure why that is, but it might explain why fake news that benefited Republicans or harmed Democrats seemed more common before the 2016 national election.   

“Fact-checking: A meta-analysis of what works and for whom”: From Northwestern University, University of Haifa, and Temple University, published in Political Communication. By Nathan Walter, Jonathan Cohen, R. Lance Holbert, and Yasmin Morag.

Even as the number of fact-checking outlets continues to grow globally, individual studies of their impact on misinformation have provided contradictory results. To better understand whether fact-checking is an effective means of correcting political misinformation, scholars from three universities teamed up to synthesize the findings of 30 studies published or released between 2013 and 2018. Their analysis reveals that the success of fact-checking efforts varies according to a number of factors. 

The resulting paper offers numerous insights on when and how fact-checking succeeds or fails. Some of the big takeaways: 

— Fact-checking messages that feature graphical elements such as so-called “truth scales” tended to be less effective in correcting misinformation than those that did not. The authors point out that “the inclusion of graphical elements appears to backfire and attenuate correction of misinformation.” 

— Fact-checkers were more effective when they tried to correct an entire statement rather than parts of one. Also, according to the analysis, “fact-checking effects were significantly weaker for campaign-related statements.” 

— Fact-checking that refutes ideas that contradict someone’s personal ideology was more effective than fact-checking aimed at debunking ideas that match someone’s personal ideology. 

— Simple messages were more effective. “As a whole, lexical complexity appears to detract from fact-checking efforts,” the authors explain.

Source: https://www.niemanlab.org/2020/01/rated-false-heres-the-most-interesting-new-research-on-fake-news-and-fact-checking/

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House Intelligence Committee chairman praised Facebook policy on deepfakes

• Other lawmakers not so impressed; add other social media platforms not doing enough

Jan 15, 2020 | Anthony Kimery

House Permanent Select Committee on Intelligence Chairman Rep. Adam Schiff (D-CA) said Facebook’s announcement this past week of its “new policy which will ban intentionally misleading deepfakes from its platforms is a sensible and responsible step, and I hope that others like YouTube and Twitter will follow suit.”

Schiff cautioned, however, that, “As with any new policy, it will be vital to see how it is implemented, and particularly whether Facebook can effectively detect deepfakes at the speed and scale required to prevent them from going viral,” emphasizing that “the damage done by a convincing deepfake, or a cruder piece of misinformation, is long-lasting, and not undone when the deception is exposed, making speedy takedowns the utmost priority.”

Schiff added he’ll “also be focused on how Facebook deals with other harmful disinformation like so-called ‘cheapfakes,’ which are not covered by this new policy because they are created with less sophisticated techniques but nonetheless purposefully and maliciously distort an existing piece of media.”

Not all lawmakers – or privacy rights advocates and groups — concerned about this problem, though, were as impressed as Schiff with Facebook’s new policy, Enforcing Against Manipulated Media, which was announcement by Facebook Vice President for Global Policy Management Monika Bickert only days before she testified last week before the House Committee on Energy and Commerce Subcommittee on Consumer Protection and Commerce hearing on, “Americans at Risk: Manipulation and Deception in the Digital Age.”

Subcommittee Chairwoman Rep. Jan Schakowsky (D-IL), chastised “Congress [for having] unfortunately taken a laissez faire approach to regulating unfair and deceptive practices online over the past decade and platforms have let them flourish,” the result of which has been “big tech failed to respond to the grave threat posed by deep-fakes, as evidenced by Facebook scrambling to announce a new policy that strikes me as wholly inadequate, since it would have done nothing to prevent the altered video of Speaker Pelosi that amassed millions of views and prompted no action by the online platform.”

Similarly, Democratic Presidential candidate Joe Biden’s spokesman Bill Russo stated, “Facebook’s announcement is not a policy meant to fix the very real problem of disinformation that is undermining face in our electoral process, but is instead an illusion of progress. Banning deepfakes should be an incredibly low floor in combating disinformation.”

Schakowsky and other subcommittee members didn’t seem much assuaged by either Bickert or the other witnesses who testified at the hearing that Facebook’s policy goes far enough.

She declared that, “Underlying all of this is Section 230 of the Communications Decency Act, which provided online platforms like Facebook a legal liability shield for 3rd party content. Many have argued that this liability shield resulted in online platforms not adequately policing their platforms, including online piracy and extremist content. Thus, here we are, with big tech wholly unprepared to tackle the challenges we face today,” which she described as “a topline concern for this subcommittee.” We “must protect consumers regardless of whether they are online or not. For too long, big tech has argued that ecommerce and digital platforms deserved special treatment and a light regulatory touch.”

In her opening statement, Schakowsky further noted that the Federal Trade Commission “works to protect Americans from many unfair and deceptive practices, but a lack of resources, authority, and even a lack of will has left many American consumers feeling helpless in the digital world. Adding to that feeling of helplessness, new technologies are increasing the scope and scale of the problem. Deepfakes, manipulated video, dark patterns, bots, and other technologies are hurting us in direct and indirect ways.”

“People share millions of photos and videos on Facebook every day, creating some of the most compelling and creative visuals on our platform,” Bickert said in announcing Facebook’s policy, but conceded “some of that content is manipulated, often for benign reasons, like making a video sharper or audio more clear. But there are people who engage in media manipulation in order to mislead,” and these “manipulations can be made through simple technology like Photoshop or through sophisticated tools that use artificial intelligence or ‘deep learning’ techniques to create videos that distort reality – usually called deepfakes.”

“While these videos are still rare on the Internet” Bickert said, “they [nevertheless] present a significant challenge for our industry and society as their use increases.”

“As we enter 2020, the problem of disinformation, and how it can spread rapidly on social media, is a central and continuing national security concern, and a real threat to the health of our democracy,” Schiff said, noting that “for more than a year, I’ve been pushing government agencies and tech companies to recognize and take action against the next wave of disinformation that could come in the form of ‘deepfakes’ — AI-generated video, audio, and images that are difficult or impossible to distinguish from real thing.”

Schiff pointed to experts who testified during an open hearing of the Intelligence Committee last year that “the technology to create deepfakes is advancing rapidly and widely available to state and non-state actors, and has already been used to target private individuals …”

Schiff said in his response to Facebook’s policy that he intends “to continue to work with government agencies and the private sector to advance policies and legislation to make sure we’re ready for the next wave of disinformation online, including by improving detection technologies, something which the recently passed Intelligence Authorization Act facilitates with a new prize competition,” which Biometric Update earlier reported on.

Bickert said Facebook’s “approach has several components, from investigating AI-generated content and deceptive behaviors like fake accounts, to partnering with academia, government and industry to exposing people behind these efforts,” underscoring that “collaboration is key. Across the world, we’ve been driving conversations with more than 50 global experts with technical, policy, media, legal, civic and academic backgrounds to inform our policy development and improve the science of detecting manipulated media,” and, “as a result of these partnerships and discussions, we are strengthening our policy toward misleading manipulated videos that have been identified as deepfakes.”

“Going forward,” she stated, Facebook “will remove misleading manipulated media if it meets the specific detailed criteria she briefly outlined in announcing the social media giant’s new policy.

She described criteria as applying specifically to content which “has been edited or synthesized – beyond adjustments for clarity or quality – in ways that aren’t apparent to an average person and would likely mislead someone into thinking that a subject of the video said words that they did not actually say, and, it is the product of artificial intelligence or machine learning that merges, replaces or superimposes content onto a video, making it appear to be authentic.”

However, she called attention to the fact that the new policy “does not extend to content that is parody or satire, or video that has been edited solely to omit or change the order of words,” highlighting that, “consistent with our existing policies, audio, photos or videos, whether a deepfake or not, will be removed from Facebook if they violate any of our other Community Standards including those governing nudity, graphic violence, voter suppression, and hate speech.”

She further stated that “videos that don’t meet these standards for removal are still eligible for review by one of our independent third-party fact-checkers, which include over 50 partners worldwide fact-checking in over 40 languages,” under the new Facebook policy. And, “If a photo or video is rated false or partly false by a fact-checker, we significantly reduce its distribution in News Feed, and reject it if it’s being run as an ad.”

“And, critically,” she stressed, “people who see it, try to share it, or have already shared it, will see warnings alerting them that it’s false.”

Bickert said the company believes that “this approach is critical to our strategy, and one we heard specifically from our conversations with experts,” exclaiming that “if we simply removed all manipulated videos flagged by fact-checkers as false, the videos would still be available elsewhere on the Internet or social media ecosystem.” Thus, she expressed, “by leaving them up and labelling them as false, we’re providing people with important information and context.”

“Our enforcement strategy against misleading manipulated media also benefits from our efforts to root out the people behind these efforts,” she continued, pointing out that, “Just last month, we identified and removed a network using AI-generated photos to conceal their fake accounts,” and Facebook “teams continue to proactively hunt for fake accounts and other coordinated inauthentic behavior.”

“We are also engaged in the identification of manipulated content, of which deepfakes are the most challenging to detect,” she continued, explaining “that’s why last September we launched the Deep Fake Detection Challenge, which has spurred people from all over the world to produce more research and open source tools to detect deepfakes.”

Meanwhile, in a separate effort by Facebook, the company has “partnered with Reuters, the world’s largest multimedia news provider, to help newsrooms worldwide to identify deepfakes and manipulated media through a free online training course,” Bickert adding, noting that “news organizations increasingly rely on third parties for large volumes of images and video, and identifying manipulated visuals is a significant challenge. This program aims to support newsrooms trying to do this work.”

She concluded by saying that, “As these partnerships and our own insights evolve, so too will our policies toward manipulated media. In the meantime, we’re committed to investing within Facebook and working with other stakeholders in this area to find solutions with real impact.”

“Facebook wants you to think the problem is video-editing technology, but the real problem is Facebook’s refusal to stop the spread of disinformation,” House Speaker Nancy Pelosi Deputy Chief of Staff Drew Hammill responded in a tweet.

Facebook was roundly chastised for seemingly only to be concerned about deepfake videos rather than all the other tech that’s been used – and admitted by Facebook — to manipulate audio and text that’s also deliberately meant to deceive viewers and readers.

“Consider the scale. Facebook has more than 2.7 billion users, more than the number of followers of Christianity. YouTube has north of 2 billion users, more than the followers of Islam. Tech platforms arguably have more psychological influence over two billion people’s daily thoughts and actions when considering that millions of people spend hours per day within the social world that tech has created, checking hundreds of times a day,” the subcommittee heard from Center for Humane Technology President and Co-Founder Tristan Harris.

“In several developing countries like the Philippines, Facebook has 100 percent penetration. Philippines journalist Maria Ressa calls it the first ‘Facebook nation.’ But what happens when infrastructure is left completely unprotected, and vast harms emerge as a product of tech companies’ direct operation and profit?”

Declaring that “social organs of society [are] left open for deception, Harris warned that “these private companies have become the eyes, ears, and mouth by which we each navigate, communicate and make sense of the world. Technology companies manipulate our sense of identity, self-worth, relationships, beliefs, actions, attention, memory, physiology and even habit-formation processes, without proper responsibility.”

“Technology,” he said, “has become the filter by which we are experiencing and making sense of the real world,” and, “in so doing, technology has directly led to the many failures and problems that we are all seeing: fake news, addiction, polarization, social isolation, declining teen mental health, conspiracy thinking, erosion of trust, breakdown of truth.”

“But, while social media platforms have become our cultural and psychological infrastructure on which society works, commercial technology companies have failed to mitigate deception on their own platforms from deception,” Harris direly warned. “Imagine a nuclear power industry creating the energy grid infrastructure we all rely on, without taking responsibility for nuclear waste, grid failures, or making sufficient investments to protect it from cyber attacks. And then, claiming that we are personally responsible for buying radiation kits to protect ourselves from possible nuclear meltdowns.”

“By taking over more and more of the ‘organs’ needed for society to function, social media has become the de facto psychological infrastructure that has created conditions that incentivize mass deception at industrialized scales,” he quantified the issue, starkly adding, “Technology companies have covertly ‘tilted’ the playing field of our individual and collective attention, beliefs and behavior to their private commercial benefit,” and that, “naturally, these tools and capabilities tend to favor the sole pursuit of private profit far more easily and productively than any ‘dual purpose’ benefits they may also have at one time — momentarily — and occasionally had for culture or society”

Hill staffers involved in this issue advised to watch for “more aggressive” legislation emanating from “the variety of committees and subcommittees” with authority “to do something.”

Indeed. Energy and Commerce Committee Chairman Frank Pallone, Jr. (D-NJ), said in his opening statement that Congress needs to move “forward to beginning to get answers “so that we can start to provide more transparency and tools for consumers to fight misinformation and deceptive practices.”

“While computer scientists are working on technology that can help detect each of these deceptive techniques, we are in a technological arms race. As detection technology improves, so does the deceptive technology. Regulators and platforms trying to combat deception are left playing whack-a-mole,” he acknowledged.

“Unrelenting advances in these technologies and their abuse raise significant questions for all of us,” he concluded, asking, “What is the prevalence of these deceptive techniques,” and, “how are these techniques actually affecting our actions and decisions?”

But, more importantly – from a distinctly legislatively regulatory position – he posited, “What steps are companies and regulators taking to mitigate consumer fraud and misinformation?”

Source: https://www.biometricupdate.com/202001/house-intelligence-committee-chairman-praised-facebook-policy-on-deepfakes

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Young people buying into ‘fake news’

By: Esther Cepeda

My son, his best friend, Dave, and I were chatting over a pizza last weekend when Dave dropped some (absolutely incorrect) information: The elderly are forgoing nursing homes for cruise ships, because the room and board cost about the same, plus you get entertainment and travel.

Again — this is not a real phenomenon. A few healthy, affluent retirees have spent a few years this way, but the cruise ship industry is in no way prepared to offer extended care for masses of frail elderly adults with complex medical conditions like chronic diseases and memory problems.

When I prompted our friend for more information, he said it made sense because cruise ships have onboard medical staff and morgues.

When further pressed — in my son’s spirited retelling, I’m described as in a rabid state, pouncing on his innocent pal — Dave said he’d definitely read a news story about it.

Errrrr, actually, he knew he’d definitely seen it somewhere.

Mmmmmm, maybe on Reddit?

My son acts like at this point I had fire blazing from my eyes. I’ll only admit that I was alarmed.

Dave is a bright young man who attended an excellent high school, just completed his first semester of college at a fancy East Coast university and is generally thoughtful and curious about the world.

But he passed on information he believed was fact because he saw “something” on a news aggregation and message board site, or “somewhere.”

This gem about retiring to a cruise ship has been around since at least 2003, according to the fact-checking site Snopes.com. It started out as a bit of viral e-lore, and there have been a few examples of real-life extended stays. But today, otherwise legitimate news-gathering organizations post branded, sponsored-content “articles” (these are paid advertisements) about how to plan such a retirement alongside real news that was reported by professional journalists and vetted by editors.

I’m not picking on a kid I care about — he’s just an example of how incredibly ill-equipped our young people are to navigate an internet that’s loaded with fake news, junk science and other “information” designed to fool them and everyone else.

In a 2018-19 national assessment of U.S. high school students, researchers at Stanford University found that two-thirds couldn’t tell the difference between reported news stories and advertisements set off by the words “sponsored content” on the homepage of a popular news website.

And more than one-third of middle school students in the U.S. said that they “rarely” or “never” learned how to judge the reliability of sources, according to an analysis of 2018 survey data from The Nation’s Report Card by the Reboot Foundation, a Paris-based nonprofit that promotes the teaching of evidence-based reasoning skills.

But while it’s clear that students must be taught media-literacy skills, there are few teachers prepared to do so. Many people, not just teachers, tend to believe that their maturity and life experience make them naturally media literate — i.e., not likely to fall for fake news or bad sources of information.

A small 2011 study of the effectiveness of teacher training on media literacy found that eight hours of in-person training — quite a lot by the common standards of professional development — prepared someone to pass on such skills. And the study also showed that, like anyone else, teachers need systematic, direct instruction on media literacy, and it must be practiced over time.

The bright side is that it’s not rocket science. For the average reader, becoming media literate is generally simple: Find some good sources, check bold assertions and be aware of any fine print, like the basis of an author’s expertise or their potential financial interest.

Now, no one can check every fact in every bit of text they read, but a high level of skepticism is warranted in this time of newsy advertisements and active disinformation campaigns. If it sounds too good (or too bad) to be true, it probably is. And since those types of pieces of “information” are what drive clicks, views and “reader engagement,” they’ve proliferated.

Do yourself and your loved ones a service, bookmark a few key fact-checking websites and use them regularly (an extensive list can be found in the appendix of the Reboot Foundation’s report, at reboot-foundation.org/fighting-fake-news).

Source: https://www.uticaod.com/opinion/20200113/young-people-buying-into-fake-news

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New tool uses AI to flag fake news for media fact-checkers

• A new artificial intelligence (AI) tool could help social media networks and news organizations weed out false stories.
• The tool uses deep-learning AI algorithms to determine if claims made in posts or stories are supported by other posts and stories on the same subject.

By: University of Waterloo

A new artificial intelligence (AI) tool could help social media networks and news organizations weed out false stories.

The tool, developed by researchers at the University of Waterloo, uses deep-learning AI algorithms to determine if claims made in posts or stories are supported by other posts and stories on the same subject.

“If they are, great, it’s probably a real story,” said Alexander Wong, a professor of systems design engineering at Waterloo. “But if most of the other material isn’t supportive, it’s a strong indication you’re dealing with fake news.”

Researchers were motivated to develop the tool by the proliferation of online posts and news stories that are fabricated to deceive or mislead readers, typically for political or economic gain.

Their system advances ongoing efforts to develop fully automated technology capable of detecting fake news by achieving 90 per cent accuracy in a key area of research known as stance detection.

Given a claim in one post or story and other posts and stories on the same subject that have been collected for comparison, the system can correctly determine if they support it or not nine out of 10 times.

That is a new benchmark for accuracy by researchers using a large dataset created for a 2017 scientific competition called the Fake News Challenge.

While scientists around the world continue to work towards a fully automated system, the Waterloo technology could be used as a screening tool by human fact-checkers at social media and news organizations.

“It augments their capabilities and flags information that doesn’t look quite right for verification,” said Wong, a founding member of the Waterloo Artificial Intelligence Institute. “It isn’t designed to replace people, but to help them fact-check faster and more reliably.”

AI algorithms at the heart of the system were shown tens of thousands of claims paired with stories that either supported or didn’t support them. Over time, the system learned to determine support or non-support itself when shown new claim-story pairs.

“We need to empower journalists to uncover truth and keep us informed,” said Chris Dulhanty, a graduate student who led the project. “This represents one effort in a larger body of work to mitigate the spread of disinformation.”

Source: https://www.sciencedaily.com/releases/2019/12/191216122422.htm