Think Again

Pakistan’s Rollercoaster Election

Is this a generationally significant change of power, or more of the same dysfunction?

May 11 is election day in Pakistan, and the prognostications are pouring in. Election predictions are always risky, and especially so in a country that is plagued by unreliable opinion polling and hasn't completed a census since 1998. Additionally, recent decentralization reform giving greater power to the country's parliament and to provincial and local authorities has empowered a dizzying array of new political actors and movements. Saturday's poll will be a confusing and complicated affair.

Predictably, the last few days alone have been filled with dramatic twists and turns. One high-profile candidate was abducted, while another was the victim of a freakish forklift accident. One vulnerable minority -- women in the Bajaur tribal agency -- was granted the right to vote, while another -- several thousand transgender people in Khyber-Pakhtunkhwa province -- was banned from doing so. Pakistan's president issued an ordinance giving overseas Pakistanis the right to vote -- after which the country's election commission announced there wouldn't be time to implement it.

With campaigning now concluded, the stories of the three major contesting parties are a study in contrast. The Pakistan People's Party (PPP), led (from abroad) by the son of Benazir Bhutto, is the battered and besieged incumbent. The Pakistan Muslim League-Nawaz (PML-N), headed by former Prime Minister Nawaz Sharif, is the conventional favorite. And the Pakistan Tehreek-e-Insaf (PTI), chaired by wildly popular ex-cricket star and populist Imran Khan, is the upstart seeking to upset them both.

Yet don't take these stock characterizations at face value. Saturday's poll, as many are saying in Pakistan, represents the most unpredictable election in Pakistan's history. That's why we should be suspicious of those bold pre-election proclamations. Here are five of the more commonly heard ones, and why they're problematic.



"The incumbent Pakistan People's Party is going to lose badly."

Not necessarily. President Asif Ali Zardari's PPP, which led the government from 2008 to 2013, is deeply unpopular. Many blame it for the violence, energy shortages, and corruption that convulsed the country over the last five years. Yet don't assume the PPP will suffer the shellacking that many are predicting. The party is, if nothing else, a survivor. It completed its term even after the Supreme Court dismissed one of its prime ministers (Yousaf Raza Gilani) and issued an arrest order for his successor (Raja Pervez Ashraf ). Most remarkably, Zardari, despite being dogged by corruption charges and despised by both Pakistan's judiciary and powerful army, remains in his post today (and could be reelected when his term ends in September). 

The PPP has pulled off electoral surprises before. In 1970, Zulfikar Ali Bhutto (father of Benazir) won a majority of seats in what was then West Pakistan -- a result few people expected from the PPP, then a new and inexperienced party.

Though many rightly expect former Prime Minister Nawaz Sharif's PML-N to triumph, a PPP election victory isn't out of the question. A second-place finish is likely. The PPP's stronghold in rural Sindh province has been spared the election violence afflicting other parts of the country, enabling it to consolidate the support of its rank and file. Additionally, Imran Khan's ascendant PTI could siphon off votes from the PML-N in the latter's stronghold of Punjab province. The PTI, drawing on its leader's popularity, populist appeals for change, and denunciations of U.S. policies, has resonated deeply with Punjab's urban youth.


"Imran Khan is poised for a big performance."

Don't bet on it. Despite the global media splash it has made, PTI's prospects shouldn't be overstated. It's true, the most recent survey data shows it tied with the PML-N as the country's most popular party, and Khan consistently polls as Pakistan's most beloved politician. The PTI also draws some of the largest crowds and social media followings of any party.

Still, Pakistan's political structure is deeply rooted in patronage -- voters tend to reward those candidates who provide them with protection, gifts, or favors. Such offerings range from promises to appease powerful landlords to the provision of scholarships and new sewage facilities. This is why many Pakistanis will often express support for one party, attend its rallies, and "like" or "follow" it in social media, and then turn around and vote for a completely different party -- that of their patron. While the PPP and PML-N enjoy extensive patronage networks, the PTI rejects the system altogether and instead relies on Khan's personal appeal -- a strategy with no track record of delivering votes (though some PTI partisans point to the charismatic Bhutto's 1970 performance as proof that it can succeed).

Additionally, one of the PTI's campaign platforms -- reducing political corruption -- may actually undercut its electoral prospects. In recent months, to underscore its own good governance intentions, the PTI held internal party elections -- a time-consuming process that obliged it to reduce its nationwide campaigning just as other parties were ramping up their own. Furthermore, several recent PTI developments -- including a rumored electoral alliance with a hardline Islamist party (which didn't pan out) and comments critical of Pakistan's persecuted Ahmadis (a minority Muslim sect) -- could alienate the party's liberal supporters. Admittedly, Khan's recent fall from a forklift -- and the inspirational hospital-bed speech that followed -- may garner his party some sympathy votes. Yet to say that his tumble could carry him to victory is a bit far-fetched.

Warrick Page/Getty Images


"U.S. interests in Pakistan are only going to deteriorate further."

It can hardly get worse. Some, pointing to the conservative, anti-American, and pro-Islamist orientation of the top-polling parties, believe this election will mark a further deterioration of already fraught U.S.-Pakistani relations. When current frontrunner Sharif was last prime minister, he triggered American sanctions after staging a nuclear test in 1998. In 2011, the PML-N-led provincial government in Punjab cancelled several U.S. civilian assistance agreements. Meanwhile, Khan has vowed to reject U.S. aid (he refers to it as a "curse") and to shoot down American drones. Though Khan is unlikely to be Pakistan's next prime minister, his party will remain a significant force in Pakistani politics -- whether through heading a ministry in the next coalition government, or, more likely, through taking on a powerful role in the political opposition. The implication is that President Barack Obama's administration should hope the PPP is reelected. It's relatively liberal and pro-American and its return would ensure some much-needed continuity for a volatile bilateral relationship.

In reality, neither U.S. interests nor U.S.-Pakistan relations are likely to be affected by the electoral outcome. Certainly a government led by the PML-N may push back more than the PPP on flashpoints like the drone program and the endgame in Afghanistan. But these issues ultimately fall within the purview of the Pakistani military, not the civilian government -- and thanks to the likes of Secretary of State John Kerry, the new Obama administration is on extremely good terms with Pakistani army leaders. For this reason, any anxiety in Washington about this Saturday's political transition would be misplaced. Much more significant is a military transition later this year, when Ashfaq Parvez Kayani -- often described as the country's most powerful leader -- ends his term as army chief.

AFP/Getty Images

"May 11 marks a watershed moment for democracy in Pakistan."

Let's not get carried away. Several weeks ago, this oft-repeated assertion was hard to dispute. May 11 marks the first time that one democratically-elected Pakistani government will hand power over to another. The election is meant to serve as an emphatic affirmation of Pakistan's growing democratic credentials.

Unfortunately, it now appears the election will be a watershed for militancy, not democracy.  The Pakistani Taliban has launched a campaign of terror against relatively liberal and non-religious parties. Dozens have died, including at least 44 in less than 10 days late last month. The leader of one major political party, the Muttahida Qaumi Movement, has demanded the election be delayed -- and authorities in two violence-riven localities have cancelled polls in their districts. The Taliban has now turned its attention to the general voting public, warning it not to turn out on May 11.

Whether the voting masses heed or defy this warning will determine if democracy is showcased or sidelined on Saturday. The electorate appears energized; a new British Council poll finds that 62 percent of Pakistanis under 30 -- an age group comprising two thirds of the country's total population -- plan to vote. Another survey projects a 10 percent increase in turnout from the 44 percent in Pakistan's last election.


"Whatever happens, Pakistan will have a new government on May 12."

Don't hold your breath. In Pakistan's parliamentary democracy, the party receiving the most votes must gain an outright majority (translating to 172 of 272 National Assembly seats) to immediately form a government. However, it's unlikely the victorious party will win by such a large margin. Neither of the two favored parties -- the PML-N and PPP -- is likely to garner enough votes in the other's stronghold. Additionally, the PTI could attract enough votes from disaffected PPP and PML-N supporters (and from the fifth of the total electorate estimated as undecided) to prevent either party from greatly distancing itself from the other.

Consequently, days and even weeks may elapse while the winning party struggles to cobble together a governing coalition. Some smaller parties are eager to serve as junior coalition members. Others -- such as the PTI, which at least for now has ruled out alliances -- could be more stubborn. As this bargaining process plays out, aggrieved losing parties may take to the streets, with unrest likely. Election watchers have expressed concern that zealous young PTI supporters could resort to violence, though the presence of militant wings within so many different parties suggest the PTI wouldn't be the sole instigator.

This transition limbo will further delay much-needed decisions on critical policy issues that have been deferred until after the election -- requesting a fresh IMF loan to revive a rapidly sinking economy, and contemplating new military operations in the tribal belt, to name a couple.

If there's one safe prediction to make on this election eve, it's that Pakistan's new leadership, when it's finally in place, will have little time to savor its victory.


Think Again

Think Again: Big Data

Why the rise of machines isn't all it's cracked up to be.

"Big data" is the jargon du jour, the tech world's one-size-fits-all (so long as it's triple XL) answer to solving the world's most intractable problems. The term is commonly used to describe the art and science of analyzing massive amounts of information to detect patterns, glean insights, and predict answers to complex questions. It might sound a bit dull, but from stopping terrorists to ending poverty to saving the planet, there's no problem too big for the evangelists of big data.

"The benefits to society will be myriad, as big data becomes part of the solution to pressing global problems like addressing climate change, eradicating disease, and fostering good governance and economic development," crow Viktor Mayer-Schönberger and Kenneth Cukier in modestly titled Big Data: A Revolution that Will Transform How We Live, Work, and Think.

So long as there are enough numbers to crunch -- whether it's data from your iPhone, grocery store purchases, online dating profile, or, say, the anonymized health records of an entire country -- the insights that can be gleaned from our computing ability to decode this raw data are innumerable. Even Barack Obama's administration has jumped with both feet on the bandwagon, releasing on May 9 a "groundbreaking" trove of "previously inaccessible or unmanageable data" to entrepreneurs, researchers, and the public.

"One of the things we're doing to fuel more private-sector innovation and discovery is to make vast amounts of America's data open and easy to access for the first time in history. And talented entrepreneurs are doing some pretty amazing things with it," said President Obama.

But is big data really all it's cracked up to be? Can we trust that so many ones and zeros will illuminate the hidden world of human behavior? Foreign Policy invited Kate Crawford of the MIT Center for Civic Media to go behind the numbers. —Ed.


"With Enough Data, the Numbers Speak for Themselves."

Not a chance. The promoters of big data would like us to believe that behind the lines of code and vast databases lie objective and universal insights into patterns of human behavior, be it consumer spending, criminal or terrorist acts, healthy habits, or employee productivity. But many big-data evangelists avoid taking a hard look at the weaknesses. Numbers can't speak for themselves, and data sets -- no matter their scale -- are still objects of human design. The tools of big-data science, such as the Apache Hadoop software framework, do not immunize us from skews, gaps, and faulty assumptions. Those factors are particularly significant when big data tries to reflect the social world we live in, yet we can often be fooled into thinking that the results are somehow more objective than human opinions. Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.

For example, social media is a popular source for big-data analysis, and there's certainly a lot of information to be mined there. Twitter data, we are told, informs us that people are happier when they are farther from home and saddest on Thursday nights. But there are many reasons to ask questions about what this data really reflects. For starters, we know from the Pew Research Center that only 16 percent of online adults in the United States use Twitter, and they are by no means a representative sample -- they skew younger and more urban than the general population. Further, we know many Twitter accounts are automated response programs called "bots," fake accounts, or "cyborgs" -- human-controlled accounts assisted by bots. Recent estimates suggest there could be as many as 20 million fake accounts. So even before we get into the methodological minefield of how you assess sentiment on Twitter, let's ask whether those emotions are expressed by people or just automated algorithms.

But even if you're convinced that the vast majority of tweeters are real flesh-and-blood people, there's the problem of confirmation bias. For example, to determine which players in the 2013 Australian Open were the "most positively referenced" on social media, IBM conducted a large-scale analysis of tweets about the players via its Social Sentiment Index. The results determined that Victoria Azarenka was top of the list. But many of those mentions of Azarenka on Twitter were critical of her controversial use of medical timeouts. So did Twitter love her or hate her? It's difficult to trust that IBM's algorithms got it right.

Once we get past the dirty-data problem, we can consider the ways in which algorithms themselves are biased. News aggregator sites that use your personal preferences and click history to funnel in the latest stories on topics of interest also come with their own baked-in assumptions -- for example, assuming that frequency equals importance or that the most popular news stories shared on your social network must also be interesting to you. As an algorithm filters through masses of data, it is applying rules about how the world will appear -- rules that average users will never get to see, but that powerfully shape their perceptions.

Some computer scientists are moving to address these concerns. Ed Felten, a Princeton University professor and former chief technologist at the U.S. Federal Trade Commission, recently announced an initiative to test algorithms for bias, especially those that the U.S. government relies upon to assess the status of individuals, such as the infamous "no-fly" list that the FBI and Transportation Security Administration compile from the numerous big-data resources at the government's disposal and use as part of their airport security regimes.


"Big Data Will Make Our Cities Smarter and More Efficient."

Up to a point. Big data can provide valuable insights to help improve our cities, but it can only take us so far. Because not all data is created or even collected equally, there are "signal problems" in big-data sets -- dark zones or shadows where some citizens and communities are overlooked or underrepresented. So big-data approaches to city planning depend heavily on city officials understanding both the data and its limits.

For example, Boston's Street Bump app, which collects smartphone data from drivers going over potholes, is a clever way to gather information at low cost, and more apps like it are emerging. But if cities begin to rely on data that only come from citizens with smartphones, it's a self-selecting sample -- it will necessarily have less data from those neighborhoods with fewer smartphone owners, which typically include older and less affluent populations. While Boston's Office of New Urban Mechanics has made concerted efforts to address these potential data gaps, less conscientious public officials may miss them and end up misallocating resources in ways that further entrench existing social inequities. One need only look to the 2012 Google Flu Trends miscalculations, which significantly overestimated annual flu rates, to realize the impact that relying on faulty big data could have on public services and public policy.

The same is true for "open government" initiatives that post data about public sectors online, such as and the White House's Open Government Initiative. More data won't necessarily improve any functions of government, including transparency or accountability, unless there are mechanisms to allow engagement between the public and their institutions, not to mention aid the government's ability to interpret the data and respond with adequate resources. None of that is easy. In fact, there just aren't many skilled data scientists around yet. Universities are currently scrambling to define the field, write curricula, and meet demand.

Human rights groups are also looking to use big data to help understand conflicts and crises. But here too there are questions about the quality of both the data and the analysis. The MacArthur Foundation recently awarded an 18-month, $175,000 grant to Carnegie Mellon University's Center for Human Rights Science to investigate how big-data analytics are changing human rights fact-finding, such as through development of "credibility tests" to sort alleged human rights violations posted to sites like Crisis Mappers, Ushahidi, Facebook, and YouTube. The director of the center, Jay D. Aronson, notes that there are "serious questions emerging about the use of data and the responsibilities of academics and human rights organizations to its sources. In many cases, it is unclear whether the safety and security of the people reporting the incidents is enhanced or threatened by these new technologies."


"Big Data Doesn't Discriminate Between Social Groups."

Hardly. Another promise of big data's alleged objectivity is that there will be less discrimination against minority groups because raw data is somehow immune to social bias, allowing analysis to be conducted at a mass level and thus avoiding group-based discrimination. Yet big data is often deployed for exactly this purpose -- to segregate individuals into groups -- because of its ability to make claims about how groups behave differently. For example, a recent paper points to how scientists are allowing their assumptions about race to shape their big-data genomics research.

As Alistair Croll writes, the potential for big data to be used for price discrimination raises serious civil rights concerns, a practice that was historically known as "redlining." Under the rubric of "personalization," big data can be used to isolate specific social groups and treat them differently, something that laws often prohibit businesses or humans from doing explicitly. Companies can choose to show online ads for a credit card offer to people who are most attractive in terms of household income or credit history to banks, leaving others completely unaware that a particular offer is available. Google even has a patent to dynamically price content: So if your past buying history indicates you are more likely to pay top dollar for shoes, your starting price the next time you shop for footwear online might be considerably higher. Now employers are trying to get apply big data to human resources, assessing how to make employees more productive, all by analyzing their every click and tap. Employees may have no idea how much data is being gathering about them or how it is being used.

Discrimination can also take on other demographic dimensions. For example, the New York Times reported that Target started compiling analytic profiles of its customers years ago; it now has so much data on purchasing trends that it can predict under certain circumstances if a woman is pregnant with an 87 percent confidence rate, simply based on her shopping history. While the Target statistician in the article emphasizes how this will help the company improve its marketing to expectant parents, one can also imagine such determinations being used in other ways to discriminate that might have serious ramifications for social equality and, of course, privacy.

And recently, a big-data study from Cambridge University of 58,000 Facebook "likes" was used to predict very sensitive personal information about users, such as sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parents' marital status, age, and gender. As journalist Tom Foremski observes of the study: "Easy access to such highly sensitive information could be used by employers, landlords, government agencies, educational institutes, and private organizations, in ways that discriminate and punish individuals. And there's no way [to] fight it."

Finally, consider the implications in the context of law enforcement. From Washington, D.C., to New Castle County, Delaware, police are turning to "predictive policing" models of big data in the hopes that they will shine investigative light on unsolved cases and even help prevent future crimes. However, focusing police activity on particular big data-detected "hot spots" runs the danger of reinforcing stigmatized social groups as likely criminals and institutionalizing differential policing as a standard practice. As one police chief has written, although predictive policing algorithms explicitly avoid categories such as race or gender, the practical result of using such systems without sensitivity to differential impact can be "a recipe for deteriorating community relations between police and the community, a perceived lack of procedural justice, accusations of racial profiling, and a threat to police legitimacy."

Tim Boyle/Getty Images

"Big Data Is Anonymous, so It Doesn't Invade Our Privacy."

Flat-out wrong. While many big-data providers do their best to de-identify individuals from human-subject data sets, the risk of re-identification is very real. Cell-phone data, on mass, may seem fairly anonymous, but a recent study on a data set of 1.5 million cell-phone users in Europe showed that just four points of reference were enough to individually identify 95 percent of people. There is a uniqueness to the way that people make their way through cities, the researchers observed, and given how much can be inferred by the large number of public data sets, this makes privacy a "growing concern." We already know, thanks to academics like Alessandro Acquisti, how to predict an individual's Social Security number simply by cross-analyzing publicly available data.

But big data's privacy problem goes far beyond standard re-identification risks. Currently, medical data sold to analytics firms has a risk of being used to track your identity. There is a lot of chatter about personalized medicine, where the hope is that drugs and other therapies will be so individually targeted that they work to heal an individual's body as if they were made from that person's very own DNA. It's a wonderful prospect in terms of improving the power of medical science, but it's fundamentally reliant on personal identification at cellular and genetic levels, with high risks if it is used inappropriately or leaked. But despite the rapid growth in personal health data collectors such as RunKeeper and Nike+, practical use of big data to improve health-care delivery is still more aspiration than reality.

Other kinds of intimate information are being collected by big-data energy initiatives, such as the Smart Grid. This effort looks to improve the efficiency of energy distribution to our homes and businesses by analyzing enormous data sets of consumer energy usage. The project has great promise but also comes with great privacy risks. It can predict not only how much energy we need and when we need it, but also minute-by-minute information on where we are in our homes and what we are doing. This can include knowing when we are in the shower, when our dinner guests leave for the night, and when we turn off the lights to go to sleep.

Of course, such highly personal big-data sets are a prime targets for hackers or leakers. WikiLeaks has been at the center of some of the most significant big-data releases of recent times. And as we saw recently with the massive data leak from Britain's offshore financial industry, the 1 percenters of the world are just as vulnerable as everyone else to having their personal data made public.


"Big Data Is the Future of Science."

Partly true, but it has some growing up to do. Big data offers new roads for science, without a doubt. We only need look to the discovery of the Higgs boson particle, a result of the largest grid-computing project in history, with CERN using the Hadoop Distributed File System to manage all the data. But unless we recognize and address some of big data's inherent weaknesses in reflecting on human lives, we may make major public policy and business decisions based on incorrect assumptions.

To address this, data scientists are starting to collaborate with social scientists, who have a long history of critically engaging with data: assessing sources, the methods of data collection, and the ethics of use. Over time, this means finding new ways to combine big-data approaches with small-data studies. This goes well beyond advertising and marketing approaches like focus groups or A/B testing (in which two versions of a design or outcome are shown to users in order to see which variation proves more effective). Rather, new hybrid methods can ask questions about why people do things, beyond just tallying up how often something occurs. That means drawing on sociological analysis and deep ethnographic insight as well as information retrieval and machine learning.

Technology companies recognized early on that social scientists could give them greater insight into how and why people engage with their products, such as when Xerox's PARC hired pioneering anthropologist Lucy Suchman. The next stage will be a richer collaboration between computer scientists, statisticians, and social scientists of many stripes -- not just to test the findings of each other's work, but to ask fundamentally different kinds of questions, with greater rigor.

Given the immense amount of information collected about us every day -- including Facebook clicks, GPS data, health-care prescriptions, and Netflix queues -- we must decide sooner rather than later whom we can trust with that information, and for what purpose. We can't escape the fact that data is never neutral and that it's difficult to anonymize. But we can draw on expertise across different fields in order to better recognize biases, gaps, and assumptions, and to rise to the new challenges to privacy and fairness.