Northern America

Computers Can Solve Your Problem. You May Not Like The Answer

Curated on September 23, 2018 by Stefaan Verhulst

David Scharfenberg at the Boston Globe: “Years of research have shown that teenagers need their sleep. Yet high schools often start very early in the morning. Starting them later in Boston would require tinkering with elementary and middle school schedules, too — a Gordian knot of logistics, pulled tight by the weight of inertia, that proved impossible to untangle.

Until the computers came along.

Last year, the Boston Public Schools asked MIT graduate students Sébastien Martin and Arthur Delarue to build an algorithm that could do the enormously complicated work of changing start times at dozens of schools — and rerouting the hundreds of buses that serve them….

The algorithm was poised to put Boston on the leading edge of a digital transformation of government. In New York, officials were using a regression analysis tool to focus fire inspections on the most vulnerable buildings. And in Allegheny County, Pa., computers were churning through thousands of health, welfare, and criminal justice records to help identify children at risk of abuse….

While elected officials tend to legislate by anecdote and oversimplify the choices that voters face, algorithms can chew through huge amounts of complicated information. The hope is that they’ll offer solutions we’ve never imagined — much as Google Maps, when you’re stuck in traffic, puts you on an alternate route, down streets you’ve never traveled.

Dataphiles say algorithms may even allow us to filter out the human biases that run through our criminal justice, social service, and education systems. And the MIT algorithm offered a small window into that possibility. The data showed that schools in whiter, better-off sections of Boston were more likely to have the school start times that parents prize most — between 8 and 9 a.m. The mere act of redistributing start times, if aimed at solving the sleep deprivation problem and saving money, could bring some racial equity to the system, too.

Or, the whole thing could turn into a political disaster.

District officials expected some pushback when they released the new school schedule on a Thursday night in December, with plans to implement in the fall of 2018. After all, they’d be messing with the schedules of families all over the city.

But no one anticipated the crush of opposition that followed. Angry parents signed an online petition and filled the school committee chamber, turning the plan into one of the biggest crises of Mayor Marty Walsh’s tenure. The city summarily dropped it. The failure would eventually play a role in the superintendent’s resignation.

It was a sobering moment for a public sector increasingly turning to computer scientists for help in solving nagging policy problems. What had gone wrong? Was it a problem with the machine? Or was it a problem with the people — both the bureaucrats charged with introducing the algorithm to the public, and the public itself?…(More)”

How Insurance Companies Used Bad Science to Discriminate

Curated on September 17, 2018October 10, 2018 by Stefaan Verhulst

Jessie Wright-Mendoza at JStor: “After the Civil War, the United States searched for ways to redefine itself. But by the 1880’s, the hopes of Reconstruction had dimmed. Across the United States there was instead a push to formalize and legalize discrimination against African-Americans. The effort to marginalize the first generation of free black Americans infiltrated nearly every aspect of daily life, including the cost of insurance.

Initially, African-Americans could purchase life insurance policies on equal footing with whites. That all changed in 1881. In March of that year Prudential, one of the country’s largest insurers, announced that policies held by black adults would be worth one-third less than the same plans held by whites. Their weekly premiums would remain the same. Benefits for black children didn’t change, but weekly premiums for their policies would rise by five cents.

Prudential defended the decision by pointing out that the black mortality rate was higher than the white mortality rate. Therefore, they explained, claims paid out for black policyholders were a disproportionate amount of all payouts. Most of the major life insurance companies followed suit, making it nearly impossible for African-Americans to gain coverage. Across the industry, companies blocked agents from soliciting African-American customers and denied commission for any policies issued to blacks.

The public largely accepted the statistical explanation for unequal coverage. The insurer’s job was to calculate risk. Race was merely another variable like occupation or geographic location. As one trade publication put it in 1891: “Life insurance companies are not negro-maniacs, they are business institutions…there is no sentiment and there are no politics in it.”

Companies considered race-based risk the same for all African-Americans, whether they were strong or sickly, educated or uneducated, from the country or the city. The “science” behind the risk formula is credited to Prudential statistician Frederick L. Hoffman, whose efforts to prove the genetic inferiority of the black race were used to justify the company’s discriminatory policies….(More)”.

The Hacking of America

Curated on September 17, 2018 by Stefaan Verhulst

Jill Lepore at the New York Times: “Every government is a machine, and every machine has its tinkerers — and its jams. From the start, machines have driven American democracy and, just as often, crippled it. The printing press, the telegraph, the radio, the television, the mainframe, cable TV, the internet: Each had wild-eyed boosters who promised that a machine could hold the republic together, or make it more efficient, or repair the damage caused by the last machine. Each time, this assertion would be both right and terribly wrong. But lately, it’s mainly wrong, chiefly because the rules that prevail on the internet were devised by people who fundamentally don’t believe in government.

The Constitution itself was understood by its framers as a machine, a precisely constructed instrument whose measures — its separation of powers, its checks and balances — were mechanical devices, as intricate as the gears of a clock, designed to thwart tyrants, mobs and demagogues, and to prevent the forming of factions. Once those factions began to appear, it became clear that other machines would be needed to establish stable parties. “The engine is the press,” Thomas Jefferson, an inveterate inventor, wrote in 1799.

The United States was founded as a political experiment; it seemed natural that it should advance and grow through technological experiment. Different technologies have offered different fixes. Equality was the promise of the penny press, newspapers so cheap that anyone could afford them. The New York Sun was first published in 1833. “It shines for all” was its common-man motto. Union was the promise of the telegraph. “The greatest revolution of modern times, and indeed of all time, for the amelioration of society, has been effected by the magnetic telegraph,” The Sun announced, proclaiming “the annihilation of space.”

Time was being annihilated too. As The New York Herald pointed out, the telegraph appeared to make it possible for “the whole nation” to have “the same idea at the same moment.” Frederick Douglass was convinced that the great machines of the age were ushering in an era of worldwide political revolution. “Thanks to steam navigation and electric wires,” he wrote, “a revolution cannot be confined to the place or the people where it may commence but flashes with lightning speed from heart to heart.” Henry David Thoreau raised an eyebrow: “We are in great haste to construct a magnetic telegraph from Maine to Texas; but Maine and Texas, it may be, have nothing important to communicate.”…

Even that savage war didn’t diminish Americans’ faith that technology could solve the problem of political division. In the 1920s, Herbert Hoover, as secretary of commerce, rightly anticipated that radio, the nation’s next great mechanical experiment, would make it possible for political candidates and officeholders to speak to voters without the bother and expense of traveling to meet them. NBC began radio broadcasting in 1926, CBS in 1928. By the end of the decade, nearly every household would have a wireless. Hoover promised that radio would make Americans “literally one people.”

That radio fulfilled this promise for as long as it did is the result of decisions made by Mr. Hoover, a Republican who believed that the government had a role to play in overseeing the airwaves by issuing licenses for frequencies to broadcasting companies and regulating their use. “The ether is a public medium,” he insisted, “and its use must be for the public benefit.” He pressed for passage of the Radio Act of 1927, one of the most consequential and underappreciated acts of Progressive reform — insisting that programmers had to answer to the public interest. That commitment was extended to television in 1949 when the Federal Communications Commission, the successor to the Federal Radio Commission, established the Fairness Doctrine, a standard for television news that required a “reasonably balanced presentation” of different political views….

All of this history was forgotten or ignored by the people who wrote the rules of the internet and who peer out upon the world from their offices in Silicon Valley and boast of their disdain for the past. But the building of a new machinery of communications began even before the opening of the internet. In the 1980s, conservatives campaigned to end the Fairness Doctrine in favor of a public-interest-based rule for broadcasters, a market-based rule: If people liked it, broadcasters could broadcast it….(More)”

Don’t forget people in the use of big data for development

Curated on September 15, 2018November 13, 2018 by Stefaan Verhulst

Joshua Blumenstock at Nature: “Today, 95% of the global population has mobile-phone coverage, and the number of people who own a phone is rising fast (see ‘Dialling up’)¹. Phones generate troves of personal data on billions of people, including those who live on a few dollars a day. So aid organizations, researchers and private companies are looking at ways in which this ‘data revolution’ could transform international development.

Some businesses are starting to make their data and tools available to those trying to solve humanitarian problems. The Earth-imaging company Planet in San Francisco, California, for example, makes its high-resolution satellite pictures freely available after natural disasters so that researchers and aid organizations can coordinate relief efforts. Meanwhile, organizations such as the World Bank and the United Nations are recruiting teams of data scientists to apply their skills in statistics and machine learning to challenges in international development.

But in the rush to find technological solutions to complex global problems there’s a danger of researchers and others being distracted by the technology and losing track of the key hardships and constraints that are unique to each local context. Designing data-enabled applications that work in the real world will require a slower approach that pays much more attention to the people behind the numbers…(More)”.

Pick your poison: How a crowdsourcing app helped identify and reduce food poisoning

Curated on September 10, 2018 by Stefaan Verhulst

Alex Papas at LATimes: “At some point in life, almost everyone will have experienced the debilitating effects of a foodborne illness. Whether an under-cooked chicken kebab, an E. coli infested salad or some toxic fish, a good day can quickly become a loathsome frenzy of vomiting and diarrhoea caused by poorly prepared or poorly kept food.

Since 2009, the website iwaspoisoned.com has allowed victims of food-poisoning victims to help others avoid such an ordeal by crowd-sourcing food illnesses on one easy-to-use, consumer-led platform.

Whereas previously a consumer struck down by food poisoning may have been limited to complaining to the offending food outlet, IWasPosioned allows users to submit detailed reports of food-poisoning incidents – including symptoms, location and space to describe the exact effects and duration of the incident. The information is then transferred in real time to public health organisations and food industry groups, who use the data to flag potentially dangerous foodborne illness before a serious outbreak occurs.

In the United States alone, where food safety standards are among the highest in the world, there are still 48 million cases of food poisoning per year. From those cases, 128,000 result in hospitalisation and 3,000 in death, according to data from the U.S. Food and Drug Association.

Back in 2008 the site’s founder, Patrick Quade, himself fell foul to food poisoning after eating a BLT from a New York deli which caused him to be violently ill. Concerned by the lack of options for reporting such incidents, he set up the novel crowdsourcing platform, which also aims at improving transparency in the food monitoring industry.

The emergence of IWasPoisoned is part of the wider trend of consumers taking revenge against companies via digital platforms, which spans various industries. In the case of IWasPoisoned, reports of foodborne illness have seriously tarnished the reputations of several major food retailers….(More)”.

How Smart Should a City Be? Toronto Is Finding Out

Curated on September 9, 2018 by Stefaan Verhulst

Laura Bliss at CityLab: “A data-driven “neighborhood of the future” masterminded by a Google corporate sibling, the Quayside project could be a milestone in digital-age city-building. But after a year of scandal in Silicon Valley, questions about privacy and security remain…

Quayside was billed as “the world’s first neighborhood built from the internet up,” according to Sidewalk Labs’ vision plan, which won the RFP to develop this waterfront parcel. The startup’s pitch married “digital infrastructure” with an utopian promise: to make life easier, cheaper, and happier for Torontonians.

Everything from pedestrian traffic and energy use to the fill-height of a public trash bin and the occupancy of an apartment building could be counted, geo-tagged, and put to use by a wifi-connected “digital layer” undergirding the neighborhood’s physical elements. It would sense movement, gather data, and send information back to a centralized map of the neighborhood. “With heightened ability to measure the neighborhood comes better ways to manage it,” stated the winning document. “Sidewalk expects Quayside to become the most measurable community in the world.”

“Smart cities are largely an invention of the private sector—an effort to create a market within government,” Wylie wrote in Canada’s Globe and Mail newspaper in December 2017. “The business opportunities are clear. The risks inherent to residents, less so.” A month later, at a Toronto City Council meeting, Wylie gave a deputation asking officials to “ensure that the data and data infrastructure of this project are the property of the city of Toronto and its residents.”

In this case, the unwary Trojans would be Waterfront Toronto, the nonprofit corporation appointed by three levels of Canadian government to own, manage, and build on the Port Lands, 800 largely undeveloped acres between downtown and Lake Ontario. When Waterfront Toronto gave Sidewalk Labs a green light for Quayside in October, the startup committed $50 million to a one-year consultation, which was recently extended by several months. The plan is to submit a final “Master Innovation and Development Plan” by the end of this year.

Palaces for the People: How Social Infrastructure Can Help Fight Inequality, Polarization, and the Decline of Civic Life

Curated on September 9, 2018 by Stefaan Verhulst

The Known Known

Curated on September 7, 2018May 29, 2019 by Stefaan Verhulst

Book Review by Sue Halpern in The New York Review of Books of The Known Citizen: A History of Privacy in Modern America by Sarah E. Igo; Habeas Data: Privacy vs. the Rise of Surveillance Tech by Cyrus Farivar; Beyond Abortion: Roe v. Wade and the Battle for Privacy by Mary Ziegler; Privacy’s Blueprint: The Battle to Control the Design of New Technologies by Woodrow Hartzog: “In 1999, when Scott McNealy, the founder and CEO of Sun Microsystems, declared, “You have zero privacy…get over it,” most of us, still new to the World Wide Web, had no idea what he meant. Eleven years later, when Mark Zuckerberg said that “the social norms” of privacy had “evolved” because “people [had] really gotten comfortable not only sharing more information and different kinds, but more openly and with more people,” his words expressed what was becoming a common Silicon Valley trope: privacy was obsolete.

By then, Zuckerberg’s invention, Facebook, had 500 million users, was growing 4.5 percent a month, and had recently surpassed its rival, MySpace. Twitter had overcome skepticism that people would be interested in a zippy parade of 140-character posts; at the end of 2010 it had 54 million active users. (It now has 336 million.) YouTube was in its fifth year, the micro-blogging platform Tumblr was into its third, and Instagram had just been created. Social media, which encouraged and relied on people to share their thoughts, passions, interests, and images, making them the Web’s content providers, were ascendant.

Users found it empowering to bypass, and even supersede, the traditional gatekeepers of information and culture. The social Web appeared to bring to fruition the early promise of the Internet: that it would democratize the creation and dissemination of knowledge. If, in the process, individuals were uploading photos of drunken parties, and discussing their sexual fetishes, and pulling back the curtain on all sorts of previously hidden personal behaviors, wasn’t that liberating, too? How could anyone argue that privacy had been invaded or compromised or effaced when these revelations were voluntary?

The short answer is that they couldn’t. And they didn’t. Users, who in the early days of social media were predominantly young, were largely guileless and unconcerned about privacy. In a survey of sixty-four of her students at Rochester Institute of Technology in 2006, Susan Barnes found that they “wanted to keep information private, but did not seem to realize that Facebook is a public space.” When a random sample of young people was asked in 2007 by researchers from the Pew Research Center if “they had any concerns about publicly posted photos, most…said they were not worried about risks to their privacy.” (This was largely before Facebook and other tech companies began tracking and monetizing one’s every move on- and offline.)

In retrospect, the tendencies toward disclosure and prurience online should not have been surprising….(More)”.

Following Fenno: Learning from Senate Candidates in the Age of Social Media and Party Polarization

Curated on September 2, 2018 by Stefaan Verhulst

David C.W. Parker at The Forum: “Nearly 40 years ago, Richard Fenno published Home Style, a seminal volume explaining how members of Congress think about and engage in the process of representation. To accomplish his task, he observed members of Congress as they crafted and communicated their representational styles to the folks back home in their districts. The book, and Fenno’s ensuing research agenda, served as a clarion call to move beyond sophisticated quantitative analyses of roll call voting and elite interviews in Washington, D.C. to comprehend congressional representation. Instead, Fenno argued, political scientists are better served by going home with members of Congress where “their perceptions of their constituencies are shaped, sharpened, or altered” (Fenno 1978, p. xiii). These perceptions of constituencies fundamentally shape what members of Congress do at home and in Washington. If members of Congress are single-minded seekers of reelection, as we often assume, then political scientists must begin with the constituent relationship essential to winning reelection. Go home, Fenno says, to understand Congress.

There are many ways constituency relationships can be understood and uncovered; the preferred method for Fenno is participant observation, which he variously terms as “soaking and poking” or “just hanging around.” Although it sounds easy enough to sit and watch, good participant observation requires many considerations (as Fenno details in a thorough appendix to Home Style). In this appendix, and in another series of essays, Fenno grapples forthrightly with the tough choices researchers must consider when watching and learning from politicians.

In this essay, I respond to Fenno’s thought-provoking methodological treatise in Home Style and the ensuing collection of musings he published as Watching Politicians: Essays on Participant Observation. I do so for three reasons: First, I wish to reinforce Fenno’s call to action. As the study of political science has matured, it has moved away from engaging with politicians in the field across the various sub-fields, favoring statistical analyses. “Everyone cites Fenno, but no one does Fenno,” I recently opined, echoing another scholar commenting on Fenno’s work (Fenno 2013, p. 2; Parker 2015, p. 246). Unfortunately, that sentiment is supported by data (Grimmer 2013, pp. 13–19; Curry 2017). Although quantitative and formal analyses have led to important insights into the study of political behavior and institutions, politics is as important to our discipline as science. And in politics, the motives and concerns of people are important to witness, not just because they add complexity and richness to our stories, but because they aid in theory generation.¹ Fenno’s study was exploratory, but is full of key theoretical insights relevant to explaining how members of Congress understand their constituencies and the ensuing political choices they make.

Second, to “do” participant observation requires understanding the choices the methodology imposes. This necessitates that those who practice this method of discovery document and share their experiences (Lin 2000). The more the prospective participant observer can understand the size of the choice set she faces and the potential consequences at each decision point in advance, the better her odds of avoiding unanticipated consequences with both immediate and long-term research ramifications. I hope that adding my cumulative experiences to this ongoing methodological conversation will assist in minimizing both unexpected and undesirable consequences for those who follow into the field. Fenno is open about his own choices, and the difficult decisions he faced as a participant observer. Encouraging scholars to engage in participant observation is only half the battle. The other half is to encourage interested scholars to think about those same choices and methodological considerations, while acknowledging that context precludes a one-size fits all approach. Fenno’s choices may not be your choices – and that might be just fine depending upon your circumstances. Fenno would wholeheartedly agree.

Finally, Congress and American politics have changed considerably from when Fenno embarked on his research in Home Style. At the end of his introduction, Fenno writes that “this book is about the early to mid-1970s only. These years were characterized by the steady decline of strong national party attachments and strong local party organizations. … Had these conditions been different, House members might have behaved differently in their constituencies” (xv). Developments since Fenno put down his pen include political parties polarizing to an almost unprecedented degree, partisan attachments strengthening among voters, and technology emerging to change fundamentally how politicians engage with constituents. In light of this evolution of political culture in Washington and at home, it is worth considering the consequences for the participant-observation research approach. Many have asked me if it is still possible to do such work in the current political environment, and if so, what are the challenges facing political scientists going into the field? This essay provides some answers.

I proceed as follows: First, I briefly discuss my own foray into the world of participant observation, which occurred during the 2012 Senate race in Montana. Second, I consider two important methodological considerations raised by Fenno: access and participation as an observer. Third, I relate these two issues to a final consideration: the development of social media and the consequences of this for the participant observation enterprise. Finally, I show the perils of social science divorced from context, as demonstrated by the recent Stanford-Dartmouth mailer scandal. I conclude with not just a plea for us to pick up where Fenno has left off, but by suggesting that more thinking like a participant observer would benefit the discipline as whole by reminding us of our ethical obligations as researchers to each other, and to the political community that we study…(More)”.