Rep. Hank Johnson on Justice Department's Siezure of AP Phone Records

The public outcry in response to the AP investigation also illustrates the public’s alarm with the lack of privacy protections for our everyday communications.  Every day, the phone records of countless Americans are subject to criminal investigations without a warrant based on probable cause.  Investigators need only a subpoena to obtain the numbers you call and receive, as well as emails and text messages that are more than 180 days old.  Warrantless surveillance brings us ever-closer to the surveillance state described by George Orwell where “every sound you made was overheard,—and, except in darkness, every moment scrutinized.”

This issue demonstrates the urgent necessity to modernize laws that have been outpaced by technology and the ease of collecting massive amounts information about Americans.  We need to modernize the Electronic Communications Privacy Act of 1986 by requiring a warrant for surveillance involving communications, phone records, and movements.  We need to update the Espionage Act of 1917 to limit prosecutions to cases involving real harms to our national security.

A New Vision for Data Science

Steven Weber writes:

It’s commonly said that most people overestimate the impact of technology in the short term, and underestimate its impact over the longer term.

Where is Big Data in 2013? Starting to get very real, in our view, and right on the cusp of underestimation in the long term. The short term hype cycle is (thankfully) burning itself out, and the profound changes that data science can and will bring to human life are just now coming into focus. It may be that Data Science is right now about where the Internet itself was in 1993 or so. That’s roughly when it became clear that the World Wide Web was a wind that would blow across just about every sector of the modern economy while transforming foundational things we thought were locked in about human relationships, politics, and social change. It’s becoming a reasonable bet that Data Science is set to do the same—again, and perhaps even more profoundly—over the next decade. Just possibly, more quickly than that.

There are important differences which have equally come into focus. Let’s face it: Data Science is just plain hard to do, in a way that the Web was not. Data is technically harder, from a hardware and a software perspective. It’s intellectually harder, because the expertise and disciplines needed to work with this kind of data span (at a minimum) computer science, statistics, mathematics, and—controversially—domain expertise in the area of application. And it will be harder to manage issues of ethics, privacy, and access, precisely because the data revolution is, well, really a revolution.

Can data, no matter how big, change the world for the better? It may be the case that in some fields of human endeavor and behavior, the scientific analysis of big data by itself will create such powerful insights that change will simply have to happen, that businesses will deftly re-organize, that health care will remake itself for efficiency and better outcomes, that people will adopt new behaviors that make them happier, healthier, more prosperous and peaceful. Maybe. But almost everything we know about technology and society across human history argues that it won’t be so straightforward.

Data Science is becoming mature enough to grapple confidently and creatively with humans, with organizations, with the power of archaic conventions that societies are stuck following. The field is broadening to a place where data science is becoming as much a social scientific endeavor as a technical one. The next generation of world class data scientists will need the technical skills to work with huge amounts of data, the analytical skills to understand how it is embedded in business and society, and the design and storytelling skills to pull these insights together and use them to motivate change.

Scoops and Software: How The New York Times Tells Stories With Data

Aron Pilhofer, director of New York Times Interactive News team:

A lot of people think that we go into databases and try to find the stories and it’s actually the exact opposite. Data is a source like any other source. It’s fallible, it’s incomplete. Just like a human being, it’s sometimes hard to know where the incompleteness and the lies are. True storytelling is a combination of narrative – you have an idea of what the story is – and then using the data as a way to support what you have already reported. A lot of the applications we build, the projects that we work on, take that same approach. It needs to tell a story first and foremost. If you can’t look at an interactive and know what the lede is and what’s the headline — If you can’t figure that out within 5 seconds, you’ve failed. I see people who get too excited about the data and not enough about the story. I see cases where there is just too much data. Narrow it down to the really key elements that people want and need and have some sense of information hierarchy, what I would think of as the lede, the nutgraf (a summary of what the story is about). If you don’t think about those things you end up with a completely indecipherable mish-mash.