MIT Sloan Distinguished Professor of Management and Professor of Marketing Catherine Tucker
From the Shorenstein Center
As a society, we have shifted from a world where policy fears are focused on the ubiquity of digital data, to one where those concerns now center on the potential harm caused by the automated processing of this data. Given this, I find it useful as an economist to investigate what leads algorithms to reach apparently biased results—and whether there are causes grounded in economics.
Excellent work from the discipline of computer science has already documented apparent bias in the algorithmic delivery of internet advertising . Recent research of mine built on this finding by running a field test on Facebook (and replicated on Google and Twitter), which revealed that an ad promoting careers in science, technology, engineering, and math (STEM) was shown to between 20 and 40 percent more men than women across different age groups . This test accounted for users from 190 different countries, with the ad displayed to at least 5,000 eyeballs in each country. In every case, the ad was specified as gender-neutral in terms of who it should be shown to.
When my team and I investigated why it was shown to far more men than women, we found that it is not because men use these internet sites more than women. Nor is it because women fail to show interest or click on these types of ads—thereby prompting the algorithm to respond to a perceived lack of interest. (In fact, our results showed that if women do see a STEM career ad, they are more likely than men to click on it.) Nor does it seem to echo any cultural bias against women in the workplace. The extent of female equality in each of the countries as measured by the World Bank was found to be empirically irrelevant for predicting this bias.
Instead, we discovered that the reason this variety of ad is shown to more men than women is because other types of advertisers actually seem to value the opportunity to get their ads in front of female (rather than male) eyeballs—and they’ll spend more to do it. Some advertisers’ willingness to pay more to show ads to women means that an ad which doesn’t specify a gender target is shown to fewer women than men. In essence, the algorithm in this case was designed to minimize costs and maximize exposure, so it shows the ad in question to fewer expensive women than what amounts to a greater number of relatively cheaper men.
Sinan Aral, MIT Sloan David Austin Professor of Management
From Harvard Business Review
In March of 2018 President Trump’s tweets claiming that Amazon pays “little or no taxes to state & local governments” sent the company’s stock toward its worst monthly performance in two years. Trump had his facts wrong — and the stock price has since recovered — but the incident highlights an unsettling problem: Companies are profoundly vulnerable to misinformation spreading on social media. Unsurprisingly, the mainstream media has focused primarily on whether false news affected the 2016 U.S. presidential election. But the truth is that nobody is safe from this kind of damage. The spread of falsity has implications for our democracies, our economies, our businesses, and even our national security. We must make a concerted effort to understand and address its spread.
For the past three years Soroush Vosoughi, Deb Roy, and I have studied the spread of false news online. (We use the label “false news” because “fake news” has become so polarizing: Politicians now use that phrase to describe news stories that don’t support their positions.) The data we collected in a recent study spanned Twitter’s history from its inception, in 2006, to 2017. We collected 126,000 tweet cascades (chains of retweets with a common origin) that traveled through the Twittersphere during this period and verified the truth or falsehood of the content that was spreading. We then compared the dynamics of how true versus false news spreads online. On March 9 Science magazine published the results of our research as its cover story.
What we found was both surprising and disturbing. False news traveled farther, faster, deeper, and more broadly than the truth in every category of information, sometimes by an order of magnitude, and false political news traveled farther, faster, deeper, and more broadly than any other type.
The importance of understanding this phenomenon is difficult to overstate. And, in all likelihood, the problem will get worse before it gets better, because the technology for manipulating video and audio is improving, making distortions of reality more convincing and more difficult to detect. The good news, though, is that researchers, AI experts, and social media platforms themselves are taking the issue seriously and digging into both the nature of the problem and potential solutions. Read More »
The spread of misinformation on social media is an alarming phenomenon that scientists have yet to fully understand. While the data show that false claims are increasing online, most studies have analyzed only small samples or the spread of individual fake stories.
My colleagues Soroush Vosoughi, Deb Roy and I set out to change that. We recently analyzed the diffusion of all of the major true and false stories that spread on Twitter from its inception in 2006 to 2017. Our data included approximately 126,000 Twitter “cascades” (unbroken chains of retweets with a common, singular origin) involving stories spread by three million people more than four and a half million times.
Disturbingly, we found that false stories spread significantly more than did true ones. Our findings were published on Thursday in the journal Science.
We started by identifying thousands of true and false stories, using information from six independent fact-checking organizations, including Snopes, PolitiFact and Factcheck.org. These organizations exhibited considerable agreement — between 95 percent and 98 percent — on the truth or falsity of these stories. Read More »
Sinan Aral, MIT Sloan David Austin Professor of Management
Our latest installment of the MIT Sloan Experts Series includes a conversation about fake news with Sinan Aral, David Austin Professor of Management and author of the forthcoming book, The Hype Machine. We’ll discuss insights from the latest research from Aral and his co-researchers Soroush Vosoughi and Deb Roy of the MIT Media Lab which overturns conventional wisdom about how misinformation spreads, what causes it to spread so fast, and who—or what—is spreading it.
It is the largest study of its kind about fake news and is featured in the latest issue of Science, “The Spread of True and False News Online”, March 9, 2018.
MIT Sloan Associate Dean of Executive Education Peter Hirst
This spring, I participated in three major IoT-focused events and came away with mixed feelings about the state of the industry. The first was the Internet of Things World in Santa Clara, California. The conference tends to focus on more technological aspects of IoT and draws thousands of attendees. A few days later, I flew to London to take part in the Internet of Things World Forum (IoTWF), an invitation-only event that caters to the C-suite audience. The third was a board meeting and strategy workshop for the Internet of Things Talent Consortium, a spinoff from the IoTWF, of which MIT Sloan Executive Education is a founding member. The three events were highly educational, thought provoking and inspirational in their own right, but all shared a common theme—we, the people, are the main barrier to faster and wider IoT adoption. Moreover, it’s the very nature of humanity, our habits and idiosyncrasies that seem to be stalling the robots’ march toward making our lives wonderfully better—or toward total world domination, depending on how you look at it. More seriously, here are some themes that emerged in my mind once the conference excitement wore off.
Stop Resisting Change People are creatures of habit. We are comfortable with what we know. It’s not laziness—it’s an evolutionary trick we’ve developed to survive as a species. New is scary and we tend to resist it, willfully or subconsciously, but this resistance can hinder progress. For example, as we heard from the main stage at IoT World Forum in London, GE— who is one of the earliest and relatively successful entrants into IoT—sees organizational inertia as one of the biggest problems in digital transformation. Culture clashes between different kinds of businesses within an organization are dragging down the entire enterprise. Traditional engineers and digital engineers are not speaking the same language. Business leaders are not yet adept in the ways of leading required to drive a digitally-enabled transformation. At IoT World, I was on a panel discussing the future leadership needs around IoT. The panel featured professionals in different industries from education to talent recruitment to defense. And what everybody was saying is that when you look at executives, the need to have agility and resilience is just as great as the need for people who can understand both the technology and the business. In the IoT era, leaders need to have awareness of all sorts of business-environment issues, as well as other important concerns, such as privacy and cyber security, regulation and public policy. Whether it’s strictly IoT or not, you could see that in examples like Uber or Airbnb, as they’ve run into public policy, regulatory and other kinds of situations. I think it’s fairly self-evident that it would be hard for those businesses to succeed without having leaders who are able to take on those kinds of aspects as well.
I’ve touched upon this subject previously, and hearing from companies like GE only reinforced my impression that there is an immediate need to not only train the workers who will work on the IoT implementation, but also to educate leaders on how to lead the transformation. In light of this, the IoT Talent Consortium is redoubling its efforts to help organizations understand and analyze that question. The group sees itself as a community where digital-transformation pioneers and people who believe that they need to or want to go through this kind of journey can share experiences and identify successful practices.