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The Atlantic By Sara M. Watson
July 1, 2014 11:39 AM

Facebook has always “manipulated” the results shown in its users’ News Feeds by filtering and personalizing for relevance. But this weekend, the social giant seemed to cross a line, when it announced that it engineered emotional responses two years ago in an “emotional contagion” experiment, published in the Proceedings of the National Academy of Sciences (PNAS).

As a society, we haven’t fully established how we ought to think about data science in practice. It’s time to start hashing that out.

Before the Data Was Big…

Data by definition is something that is taken as “given,” but somehow we’ve taken for granted the terms under which we came to agree that fact. Once, the professional practice of “data science” was called business analytics. The field has now rebranded as a science in the context of buzzwordy “Big Data,” but unlike other scientific disciplines, most data scientists don’t work in academia. Instead, they’re employed in commercial or governmental settings.

The Facebook Data Science team is a prototypical data science operation. In the company’s own words, it collects, manages, and analyzes data to “drive informed decisions in areas critical to the success of the company, and conduct social science research of both internal and external interest.” Last year, for example, it studied self-censorship—when users input but do not post status updates. Facebook’s involvement with data research goes beyond its in-house team. The company is actively recruiting social scientists with the promise of conducting research on “recording social interaction in real time as it occurs completely naturally.” So what does it mean for Facebook to have a Core Data Science Team, describing their work—on their own product—as data science?

Contention about just what constitutes science has been around since the start of scientific practice. By claiming that what it does is data science, Facebook benefits from the imprimatur of an established body of knowledge. It looks objective, authoritative, and legitimate, built on the backs of the scientific method and peer review. Publishing in a prestigious journal, Facebook legitimizes its data collection and analysis activities by demonstrating their contribution to scientific discourse as if to say, “this is for the good of society.”

So it may be true that Facebook offers one of the largest samples of social and behavioral data ever compiled, but all of its studies—and this one, on social contagion—only describe things that happen on Facebook. The data is structured by Facebook, entered in a status update field created by Facebook, produced by users of Facebook, analyzed by Facebook researchers, with outputs that will affect Facebook’s future News Feed filters, all to build the business of Facebook. As research, it is an over-determined and completely constructed object of study, and its outputs are not generalizable.

Ultimately, Facebook has only learned something about Facebook.

The Wide World of Corporate Applied Science

For-profit companies have long conducted applied science research. But the reaction to this study seems to suggest there is something materially different in the way we perceive commercial data science research’s impacts. Why is that?

At GE or Boeing, two long-time applied science leaders, the incentives for research scientists are the same as they are for those at Facebook. Employee-scientists at all three companies hope to produce research that directly informs product development and leads to revenue. However, the outcomes of their research are very different. When Boeing does research, it contributes to humanity’s ability to fly. When Facebook does research, it serves its own ideological agenda and perpetuates Facebooky-ness.

Facebook is now more forthright about this. In a response to the recent controversy, Facebook data scientist Adam Kramer wrote, “The goal of all of our research at Facebook is to learn how to provide a better service…We were concerned that exposure to friends’ negativity might lead people to avoid visiting Facebook. We didn’t clearly state our motivations in the paper.”

Facebook’s former head of data science Cameron Marlow offers, “Our goal is not to change the pattern of communication in society. Our goal is to understand it so we can adapt our platform to give people the experience that they want.”

But data scientists don’t just produce knowledge about observable, naturally occurring phenomena; they shape outcomes. A/B testing and routinized experimentation in real time are done on just about every major website in order to optimize for certain desired behaviors and interactions. Google designers infamously tested up to 40 shades of blue. Facebook has already experimented with the effects of social pressure in getting-out-the-vote, raising concerns about selective digital gerrymandering. What might Facebook do with its version of this research? Perhaps it could design the News Feed to show us positive posts from our friends in order to make us happier and encourage us to spend more time on the site? Or might Facebook show us more sad posts, encouraging us to spend more time on the site because we have more to complain about?

Should we think of commercial data science as science? When we conflate the two, we assume companies are accountable for producing generalizable knowledge and we risk according their findings undue weight and authority. Yet when we don’t, we risk absolving practitioners from the rigor and ethical review that grants authority and power to scientific knowledge.

Facebook has published a paper in an attempt to contribute to the larger body of social science knowledge. But researchers today cannot possibly replicate Facebook’s experiment without Facebook’s cooperation. The worst outcome of this debacle would be for Facebook to retreat and avoid further public relations fiascos by keeping all its data science research findings internal. Instead, if companies like Facebook, Google, and Twitter are to support an open stance toward contributing knowledge, we need researchers with non-commercial interests who can run and replicate this research outside of the platform’s influence.

Facebook sees its users not as a population of human subjects, but as a consumer public. Therefore, we—that public and those subjects—must ask the bigger questions. What are the claims that data science makes both in industry and academia? What do they say about the kinds of knowledge that our society values?

We need to be more critical of the production of data science, especially in commercial settings. The firms that use our data have asymmetric power over us. We do them a favor unquestioningly accepting their claims to the prestige, expertise, and authority of science as well.

Ultimately, society’s greatest concerns with science and technology are ethical: Do we accept or reject the means by which knowledge is produced and the ends to which it is applied? It’s a question we ask of nuclear physics, genetic modification—and one we should ask of data science.


June 11, 2013 2:41 PM ET
Big Data may not be much to look at, but it can be powerful stuff. For instance, this is what the new National Security Agency (NSA) data center in Bluffdale, Utah, looks like.

Big Data may not be much to look at, but it can be powerful stuff. For instance, this is what the new National Security Agency (NSA) data center in Bluffdale, Utah, looks like.

George Frey/Getty Images

New technologies are not all equal. Some do nothing more than add a thin extra layer to the top-soil of human behavior (i.e., Teflon and the invention of non-stick frying pans). Some technologies, however, dig deeper, uprooting the norms of human behavior and replacing them with wholly new possibilities. For the last few months I have been arguing that Big Data — the machine-based collection and analysis of astronomical quantities of information — represents such a turn. And, for the most part, I have painted this transformation in a positive light. But last week’s revelations about the NSA’s PRISM program have put the potential dangers of Big Data front and center. So, let’s take a peek at Big Data’s dark side.

The central premise of Big Data is that all the digital breadcrumbs we leave behind as we go about our everyday lives create a trail of behavior that can be followed, captured, stored and “mined” en-mass, providing the miners with fundamental insights into both our personal and collective behavior.

The initial “ick” factor from Big Data is the loss of privacy, as pretty much every aspect of your life (location records via mobile phones, purchases via credit cards, interests via web-surfing behavior) has been recorded — and, possibly, shared — by some entity somewhere. Big Data moves from “ick” to potentially harmful when all of those breadcrumbs are thrown in a machine for processing.

This is the “data-mining” part of Big Data and it happens when algorithms are used to search for statistical correlations between one kind of behavior and another. This is where things can get really tricky and really scary.

Consider, for example, the age-old activity of securing a loan. Back in the day you went to a bank and they looked at your application, the market and your credit history. Then they said “yes” or “no.” End of story. In the world of Big Data, banks now have more ways to assess your credit worthiness.

“We feel like all data is credit data,” former Google CIO Douglas Merrill said last year in The New York Times. “We just don’t know how to use it yet.” Merrill is CEO of ZestCash, one of a host of start-up companies using information from sources such as social networks to determine the probability that an applicant will repay their loan.

Your contacts on LinkedIn can be used to assess your “character and capacity” when it comes to loans. Facebook friends can also be useful. Have rich friends? That’s good. Know some deadbeats, not so much. Companies will argue they are only trying to sort out the good applicants from the bad. But there is also a real risk that you will be unfairly swept into an algorithm’s dead zone and disqualified from a loan, with devastating consequences for your life.

Jay Stanley of the ACLU says being judged based on the actions of others is not limited to your social networks:

Credit card companies sometimes lower a customer’s credit limitbased on the repayment history of the other customers of stores where a person shops. Such “behavioral scoring” is a form of economic guilt-by-association based on making statistical inferences about a person that go far beyond anything that person can control or be aware of.

The link between behavior, health and health insurance is another gray (or dark) area for Big Data. Consider the case of Walter and Paula Shelton of Gilbert, Louisiana. Back in 2008, Business Weekreported how the Sheltons were denied health insurance when records of their prescription drug purchases were pulled. Even though their blood pressure and anti-depression medications were for relatively minor conditions, the Sheltons had fallen into another algorithmic dead zone in which certain kinds of purchases trigger red flags that lead to denial of coverage.

Since 2008 the use of Big Data by the insurance industry has only become more entrenched. As The Wall Street Journal reports:

Companies also have started scrutinizing employees’ other behavior more discreetly. Blue Cross and Blue Shield of North Carolina recently began buying spending data on more than 3 million people in its employer group plans. If someone, say, purchases plus-size clothing, the health plan could flag him for potential obesity—and then call or send mailings offering weight-loss solutions.

Of course no one will argue with helping folks get healthier. But with insurance costs dominating company spreadsheets, it’s not hard to imagine how that data about plus-size purchases might someday factor into employment decisions.

And then there’s the government’s use, or misuse, of Big Data. For years critics have pointed to no-fly lists as an example of where Big Data can go wrong.

No-fly lists are meant to keep people who might be terrorists off of planes. It has long been assumed that data harvesting and mining are part of the process for determining who is on a no-fly list. So far, so good.

But the stories of folks unfairly listed are manifold: everything from disabled Marine Corps veterans to (at one point) the late Sen. Ted Kennedy. Because the methods used in placing people on the list are secret, getting off the list can, according to Connor Freidersdorf of The Atlantic, be a Kafka-esque exercise in frustration.

A 2008 National Academy of Sciences report exploring the use of Big Data techniques for national security made the dangers explicit:

The rich digital record that is made of people’s lives today provides many benefits to most people in the course of everyday life. Such data may also have utility for counterterrorist and law enforcement efforts. However, the use of such data for these purposes also raises concerns about the protection of privacy and civil liberties. Improperly used, programs that do not explicitly protect the rights of innocent individuals are likely to create second-class citizens whose freedoms to travel, engage in commercial transactions, communicate, and practice certain trades will be curtailed—and under some circumstances, they could even be improperly jailed.

So where do we go from here?

From credit to health insurance to national security, the technologies of Big Data raise real concerns about far more than just privacy (though those privacy concerns are real, legitimate and pretty scary). The debate opening up before us is an essential one for a culture dominated by science and technology.

Who decides how we go forward? Who determines if a technology is adopted? Who determines when and how it will be deployed? Who has the rights to your data? Who speaks for us? How do we speak for ourselves?

These are the Big Questions that Big Data is forcing us to confront.

Greetings millennials. Modern doctors are having difficulties fighting bacteria in hospitals everywhere due to new strains of bacteria that are resistant to even the strongest antibiotics in our medicine cabinets. How? Bacteria have adapted to the antibiotics and have become immune to them. Bacteria have been conditioning for this a while now. According to this article when a lot of people took antibiotics in the ’50s and ’60s, there was a lot of talk about the new “miracle drugs” and “wonder drugs”. We have stopped our evolutionary forces that help us adapt to new infections and bacteria. We are in a society where we essentially found a way to avoid infectious diseases but this has backfired on us. Who is this going to affect? The millennials of the world, our children and our children’s children. Do we see ourselves in the near future in a world where even the most minuscule cut can kill you? Unless we find quick medicinal alternatives and spark more conversations about this amongst medical leaders of the world we should not be looking the other way.


Antibiotics Can’t Keep Up With ‘Nightmare’ Superbugs
Courtesy of Centers for Disease Control and Prevention

Published: October 22, 2013

We’re used to relying on antibiotics to cure bacterial infections. But there are now strains of bacteria that are resistant to even the strongest antibiotics, and are causing deadly infections. According to the CDC, “more than 2 million people in the United States every year get infected with a resistant bacteria, and about 23,000 people die from it,” journalist David Hoffman tells Fresh Air’s Terry Gross.

Many people are familiar with the type of resistant infections often acquired in hospitals, caused by MRSA, the acronym for methicillin-resistant Staphylococcus aureus. But most people don’t know about the entirely different group of resistant bacteria that Hoffman reports on in Hunting the Nightmare Bacteria, airing Tuesday on PBS’ Frontline. The show explores an outbreak of resistant bacteria at one of the most prestigious hospitals in the U.S., and explains why there is surprisingly little research being conducted into new antibiotics to combat these new superbugs.

“We really have a big information black hole about these really, really dangerous bacteria, and we need to know more, and it ought to be a national priority,” Hoffman says.

Interview Highlights

On how bacteria have evolved to be resistant to our antibiotics

Bacteria have been training at this for a long, long time. I think when a lot of people took antibiotics in the ’50s and ’60s, there was a lot of talk then about “miracle drugs” and “wonder drugs” … Had we basically pushed back those evolutionary forces? Had we essentially found a way to avoid infectious disease? Well, what we’re seeing is this evolutionary process in bacteria. It’s relentless, and what happened here was [that] bacteria learned to basically teach each other to swap these enzymes and help each other learn how to beat back our best antibiotics; our last-resort antibiotics didn’t work. …

In the period before World War II … people that got infections, they had to cut it out. They had to cut off limbs, cut off toes, because there weren’t antibiotics. And oftentimes, when people talk about the fact that we might have to go back to a pre-antibiotic age, that’s what they mean — that a simple scrape on the playground could be fatal.

On how pharmaceutical companies don’t have economic incentive to develop new antibiotics

[In the '50s and '60s] I think there was something like 150 classes of new antibiotics. And although there were warnings then that if we misused them that resistance would grow, you could just see in the marketplace new ones coming on every couple of years. … I think we got very, very complacent. … In the ’80s and particularly in the ’90s we went around the bend a little bit because the science didn’t continue to produce new antibiotics at that rate, and the economics of drug development changed rather remarkably. …

We’re bombarded with advertisements that there are drugs now to treat chronic diseases … that you would take for the rest of your life. And you can imagine, if you’re in drug development, if you create and invent one of these drugs that can tackle a chronic disease that people will take forever, the return on investment for the drug companies to develop those big blockbuster drugs … that became irresistible.

But think about antibiotics. If they’re taken properly, you take them only for a short course, a couple of weeks maybe, and then you stop and you forget it, you get better. … So the economics of making antibiotics wasn’t going to make these big profits for the drug companies. And slowly, but with increasing frequency, they begun to pull out of research on antibiotics.

On what’s unique about these new strains of bacteria that makes them resistant to antibiotics

One feature of [some antibiotic-resistant] bacteria is that they have kind of a hard armor around them, a shell, which protects them from antibiotics. They also have the ability to pump out the antibiotics or to basically chew it up inside. These … are very, very difficult to treat; you just don’t have antibiotics for them. …

NDM-1 [an enzyme that makes bacteria antibiotic-resistant] has a very, very unusual but worrisome characteristic, and that is this: It has a mechanism to transfer its genes — its genetic material — which helps it resist antibiotics. It can transform that to other bugs. It can walk around like a coach, giving training and directions to other bacteria … helping other bacteria become resistant to antibiotics … teaching them how to do it. …

NDM-1 is now spreading in the United States. … There have been 16 cases, and the year before the numbers doubled in a year. You know, it’s not an epidemic or anything, but these things are popping up now with more and more frequency in hospitals and in patients around the United States. It’s here.

On the bacterial outbreak at the National Institutes of Health Clinical Center and how it spread

This clinical center is a very sophisticated research hospital. Patients are invited there because of research programs going on in the many different institutes of the National Institutes of Heath. It’s not a hospital you walk into with a sore throat.

They had enrolled and invited a patient from New York, a woman who had had a major operation, and she was invited for a particular study that they needed to do. When they came they looked at her chart, and they had no warning, but the chart said that this woman had this drug-resistant bacteria … which they had never seen before. This is a resistant [bacteria] called KPC; it’s resistant to some of our most modern, last resort … antibiotics. They did what they could to prevent this bacteria from spreading. They put her in what’s called “enhanced contact isolation.” Gloves and gowns for everybody, put her at the end of the ICU in her own room, and they went through with the research they had to do. And after a month … she seemed to survive it; she was discharged. Everybody at the clinical center breathed a sigh of relief. …

Then there was a couple weeks of quiet. And then a really, really, surprising thing happened. This particular bacteria started to show up in other patients in routine surveillance of the patients. Suddenly, in the microbiology lab they’re beginning to see this thing, and none of these other patients had any contact with the first one. … So where was this bug? Where was the mechanism that it was moving around? And that was a real crisis. …

So they went through a series of phases of this war that they carried out to try to deal with this. The first thing that they thought was: Maybe it’s just in the environment; maybe it was on door handles; maybe it was on a doctor’s hands or gloves. … So they began to clean a lot of things. … They did everything they could to bleach and clean and make sure they could stop it, and they still didn’t stop it; it started to continue to spread. …

[They approached a team of genomics specialists, and] the people in genomics had some very tentative early experience of taking a bacteria and taking a look at its entire genetic blueprint. … So they took a couple samples of this bug that was racing around … and they started running it through the computers. It takes a while, it took a couple of weeks, but when they got the results back, everybody was completely stunned. … The results showed that the bug had jumped from patient No. 1 to several other people, and it had jumped by being carried by people who didn’t get sick from it. They found out that there were intermediaries, or silent carriers, that were spreading it around. To this day we don’t know [if] the silent carriers were other patients [or] hospital workers. They began to see that this wasn’t so much a problem of KPC being in the environment as it was people were moving it around.

On how hospitals aren’t required to report outbreaks of bacteria resistant to antibiotics

In other countries, take Canada for example, outbreaks are routinely announced by the hospitals. In the European Union they are tracking the rise of resistant [bacteria] in a way that we aren’t … I think a little more better tracking, better information and transparency would make us all more sensitive to these things and maybe wake us up a little bit from this complacency that we got to “miracle drugs” in the ’50s and ’60s, and realize [that] this is a very, very precarious and precious thing we have. We need to act that way. …

The data, the information on this entire thread is very, very slim. There is not a national surveillance system reporting to the CDC on these new kinds of infections, and it’s something we really need. I think it’s something the government knows we need, and we don’t have it. There are a few states that require reporting on these kinds of infections. A few require some hospitals to report, and you know, after last March when the head of the CDC first spoke out and called these things “nightmare bacteria” a couple more states signed up and required their hospitals to begin reporting. You can’t go to any single place and find out what’s going on. … The last I checked only about 16 hospitals were participating. [Copyright 2013 NPR]


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