Big Data, Big Trouble

big dataEqual parts mathematician and political activist, Cathy O’Neil has calculated the impact of algorithms on society. For the most part, she says, big data adds up to big trouble.

When it comes to human activities, algorithms are expected to be models of objectivity, owing to their basis in mathematical formulae and reliance on enormous quantities of measured facts about a given general population, whether students or teachers, job applicants or criminal defendants.

Cathy O’Neil makes the case that real-world mathematical models are anything but objective. In her new book, Weapons of Math Destruction, she asserts that big data WMDs are opaque, unaccountable and destructive and that they essentially act as unwritten and unpublished secret laws. Weapons of Math Destruction was long-listed for a National Book Award in nonfiction and was published in September, 2016, to enthusiastic reviews from the likes of Clay Shirky and Cory Doctorow.

“It’s really not a book about math,” she tells Copyright Clearance Center’s Chris Kenneally. “I know a lot of people worry about that, but the way I describe it is [that] it’s a book about power.

“In particular, it’s a book about the way that people with power are building tools of social control and shielding those tools from scrutiny by saying, ‘this is mathematics, you’re not an expert in mathematics, so you wouldn’t understand it.’ So in other words, they’re flashing the ‘math ID,’ just as you might see a policeman flash a badge, and saying, ‘this is something that you can’t ask questions about.’

“My book is about looking past that shield,” she continues, “and saying, ‘yeah, we actually have every right as human beings to ask questions about things that affect us very deeply, that are secret, and [that] are possibly quite unfair and destructive.”

To those concerned for the future of publishing, the arrival of Big Data requires carefully considered choices about best practices.

“Big data is sort of taking over the publishing world… But as in every other way big data is taking things over, there’s always this kind of choice of how you define success,” she explains.
“And probably for publishing – I’m just guessing – success looks like number of copies sold, right? But if you’ve defined that as success and you optimize to the number of copies sold or whatever the book – or the number of subscribers or something along those lines, then you actually lose, over time, if you’re optimizing only to that and focusing only on that, then you’re actually probably losing value in other ways.

“Publishers probably also care about how many awards that book was nominated for, or whether the book actually was high quality,” O’Neil adds. “But if you’re focusing only on the number of books sold, then you’re going to be blind to those other kinds of things that you do find valuable but which are harder to measure.”

You can download this podcast episode here or visit Beyond the Book to play it online.

This article originally appeared on Beyond the Book.

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2 thoughts on “Big Data, Big Trouble

  1. Michael W. Perry

    One episode in the 1967 British TV series, “The Prisoner” was about the island’s prisoners fighting back by jamming those spying on them with numerous false plots to escape. Faced with so many plots, determining which was real became more difficult.

    Big Data efforts to probe our lives could be countered by similar jamming. Anti-big data apps could browse Amazon (for instance) in the background or when we were out, giving every appearance that we’re looking at particular books or items we have no interest in. Fed so much bogus data, it’d be difficult for Amazon—or any other manipulator of big data—to discover the real us. The downside is that Amazon and others can ignore the noise of webpage visits and derive their big data only from purchases. It’d be less comprehensive, but it would work.

    Where this gets scary is in politics. Parties and candidates could use big data to target individual voters, adjusting what is said based on that data has told them about individuals.

  2. Dorothy Sanchez

    This is why it is so important to have good managers. Companies should be more careful about the people that are placed in management positions. Here is a case scenario: There are some people that believe that African American people are supposed to be poorer than others. It is a cultural belief. They will do anything to make sure that they are pulled down and stay in the lowest possible income level. They will not offer the proper rate salary to African Americans. They do not believe black people should be allowed to make as much as others. Now, let’s change the case scenario. Let’s change African Americans with Women, Gays, Lesbians, Fat People, Indian People, Handicap People, White People, Veterans, Poor People, Old People, Chinese People, and Married People, etc. Do you see the horrible picture? I am not against other people’s belief system. However, if your beliefs are hurting other people’s lives and you are placed into a management position, it speaks very badly on the company. The company may be displaying characteristics it was not intending to do. If you are interviewing people for jobs and you know that someone should have been selected but the black box algorithm states differently, your judgment should override the black box. The tests that companies are giving to select the best candidate for the job are mostly useless. Even if the candidate gets all the questions wrong, it still does not tell you if the candidate will not make a good job hire. The problem here is we are too fixed on rejecting people than accepting people. We are looking for too many reasons to reject. Maybe it is because we want the power of feeling special. The truth is you are no different than others. We all want to survive and live life to the fullest. But there should be a limit in the thing we will do to make that happen. It is so easy to put all our prejudices in a black box and not be accountable for them. Ignore the lives of others, while building a fortune. This is what has happen.



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