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Topic: #workplace-surveillance

Hyper-Analytic Management

2018-02-24⊺16:40:22-06:00

I must have missed this article when it first appeared.

“They're Watching You at Work”
Don Peck, The Atlantic, December 2013
https://www.theatlantic.com/magazine/archive/2013/12/theyre-watching-you-at-work/354681/

One of the main topics is the now-common use of surveillance technologies to micromanage employees and micromonitor their activities and every aspect of their performance:

Torrents of data are routinely collected by American companies and now sit on corporate servers, or in the cloud, awaiting analysis. Bloomberg reportedly logs every keystroke of every employee, along with their comings and goings in the office. The Las Vegas casino Harrah's tracks the smiles of the card dealers and waitstaff on the floor (its analytics team has quantified the impact of smiling on customer satisfaction). E-mail, of course, presents an especially rich vein to be mined for insights about our productivity, our treatment of co-workers, our willingness to collaborate or lend a hand, our patterns of written language, and what those patterns reveal about our intelligence, social skills, and behavior. As technologies that analyze language become better and cheaper, companies will be able to run programs that automatically trawl through the e-mail traffic of their workforce, looking for phrases or communication patterns that can be statistically associated with various measures of success or failure in particular roles.

An even more thought-provoking passage deals with data mining as a method of distinguishing candidates for software-development positions:

This past summer, I sat in on a sales presentation by Gild, a company that uses people analytics to help other companies find software engineers. I didn't have to travel far: Atlantic Media, the parent company of The Atlantic, was considering using Gild to find coders. …

The company's algorithms begin by scouring the Web for any and all open-source code, and for the coders who wrote it. They evaluate the code for its simplicity, elegance, documentation, and several other factors, including the frequency with which it's been adopted by other programmers. For code that was written for paid projects, they look at completion times and other measures of productivity. Then they look at questions and answers on social forums such as Stack Overflow, a popular destination for programmers seeking advice on challenging projects. They consider how popular a given coder's advice is, and how widely that advice ranges.

The algorithms go farther still. They assess the way coders use language on social networks from LinkedIn to Twitter; the company has determined that certain phrases and words used in association with each other can distinguish expert programmers from less skilled ones. Gild knows these phrases and words are associated with good coding because it can correlate them with its evaluation of open-source code, and with the language and online behavior of programmers in good positions at prestigious companies.

Here's the part that's most interesting: having made those correlations, Gild can then score programmers who haven't written open-source code at all, by analyzing the host of clues embedded in their online histories. They're not all obvious, or easy to explain. Vivienne Ming, Gild's chief scientist, told me that one solid predictor of strong coding is an affinity for a particular Japanese manga site.

Why would good coders (but not bad ones) be drawn to a particular manga site? By some mysterious alchemy, does reading a certain comic-book series improve one's programming skills? “Obviously, it's not a causal relationship,” Ming told me. But Gild does have 6 million programmers in its database, she said, and the correlation, even if inexplicable, is quite clear. …

Gild's CEO, Sheeroy Desai, told me that he believes his company's approach can be applied to any occupation characterized by large, active online communities, where people post and cite individual work, ask and answer professional questions, and get feedback on projects.

It cheers me somewhat to report that Gild appears to have gone out of business in 2016.

#data-mining #micromanagement #workplace-surveillance

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John David Stone (havgl@unity.homelinux.net)

created June 1, 2014 · last revised December 10, 2018