July 12th, 2010
by Anisha Sekar
The news seems rife with opportunities for crowdsourcing in government.
We’ve already noted how crowdsourcing could aid federal investigators digging into Goldman Sachs transactions (and even offered to categorize and tag the first 100,000 documents for free).
Now comes The New York Times story of companies skirting the House of Representatives’ recent ban on earmarks to for-profits by creating strikingly similar nonprofits that, of course, the ban exempts.
Some lawmakers are clamoring to take advantage of this loophole — already racking up $150 million in earmark requests for these newly minted nonprofits.
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July 8th, 2010
by Stephanie Geerlings
CrowdFlower Office
On July 19, the following speakers will come to CrowdFlower to discuss developments in modern crowdsourcing work:
is a freelance journalist, who focuses on the relationships between science, technology, and society. He has written for numerous publications, including The Atlantic, the New York Times, New Scientist, and The Guardian.
is an Assistant Professor in the Computer Science Department at Stanford University, where he works on human-computer interaction, visualization, and social computing.
Patrick McKenna
is founder and former vice president of LiveOps, an on-demand, crowdsourced calling center. He is also founder and CEO of Keniks.
Please RSVP at Meetup.com and Plancast.
Where:
CrowdFlower Office
455 Valencia Street
San Francisco, CA 94103
When:
July 19 from 5:00 p.m. – 8:00 p.m.
June 29th, 2010
by Alek Felstiner
In previous posts, I discussed the nature of employment law as it relates to crowd work, and the problems involved in trying to classify crowd workers according to existing categories and in transferring rights of free assembly and collective action into virtual space. Now comes the controversial part: explaining why I think it’d be a good idea for the law to jump into the middle of this complicated mess and start telling people what to do.
For some lawyers and lawmakers, “because we can” is a good enough reason. Others might press for regulation because advising clients in a regulation-free market generates fewer billable hours. But for a moment, let’s at least pretend that we as a society ought to engage in some kind of critical inquiry before intervening in an as-yet unregulated industry. And, while we’re pretending, let’s presume that such an inquiry would be shaped not by political dynamics but by the best information we have regarding how the law works and how regulation affects economic and social activity.
I’m not an economist, so I won’t be discussing the potential influence of economic theory on regulatory policy in this area. Instead I’ll focus on how the law deals with scenarios, like this one, in which existing doctrine appears woefully ill-equipped. The first question should always be: Does a problem actually exist? (Contrary to what you may believe, many lawyers and judges are perfectly willing to leave well enough alone. We’re not all “activists,” and in some cases, the most activist thing one can do is to permit the unfettered private ordering of employment relationships.)
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June 22nd, 2010
by Josh Eveleth
When federal investigators asked Goldman Sachs for its transactions with insurance giant AIG, Goldman turned over the information — several hundred billion pages’ worth.
John Carney, senior editor at CNBC.com, had an idea for sifting through the data — crowdsource it.
We agree. In fact, CrowdFlower will categorize and tag the first 100,000 documents at no cost to the government.
If you’re just tuning in, the federal Financial Crisis Inquiry Commission (FCIC) subpoenaed Goldman for its AIG transactions, following accusations that Goldman cooked up a mortgage investment scheme that was rigged to fail.
FCIC has around 50 employees, an $8 million budget, and roughly six months to pore over the five terabytes of data. (Can you say, “Too small to succeed”?)
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June 9th, 2010
by Stephanie Geerlings
IEEE presents the 23rd Computer Vision and Pattern Recognition Conference on June 13th – 18th. Registration required.
Computer vision researchers work hard to replicate human ability to understand images. It makes sense they are excited about crowdsourcing. Now they can ask hundreds of thousands of people to interpret a few millions images. It becomes possible to scale datasets, collect new types of data and design novel algorithms that rely on people in the loop.
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