Archive for September, 2013

September 24th 2013

The Risk of Prejudice in Computerized Prediction

I originally published this article in Profiles in Diversity Journal. The article relates to my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

The Risk of Prejudice in Computerized Prediction

Today’s predictive technology introduces a new risk of prejudice on a massive, automated scale. Predictive analytics predicts what each person will do so that companies and government agencies can operate more effectively. In some cases, this technology does drive life-changing decisions. Large organizations like Hewlett-Packard inform human resource decisions by predicting whether each employee is likely to quit, and states such as Oregon and Pennsylvania analytically predict whether each convict will commit crime again (recidivism) in order to make sentencing and parole decisions.

Given this influence on the lives of individuals, predictive analytics introduces a new risk of prejudice in two ways:

1. Prediction of minority status. Fueled with data, computers automatically detect one’s minority status. A new study from the University of Cambridge shows that race, age, and sexual orientation can be accurately determined by one’s Facebook likes. The capacity to predict grants marketers and other researchers access to unvolunteered demographic information. Some such personnel may be keen on managing and using this information appropriately, but have not necessarily been trained to do so.

2. Prediction with minority status. When utilizing predictive analytics, it is difficult to avoid incorporating minority status into the predictive model as one basis of prediction. There is no place this threat is more apparent than in law enforcement, where computers have become respected advisers that have the attention of judges and parole boards.

While science promises to improve the effectiveness of law enforcement, when the organization formalizes and quantifies decision making, it inadvertently instills existing prejudices against minorities. Why? Because prejudice is cyclical, a self-fulfilling prophecy, and this cycling could be intensified by the deployment of predictive analytics.

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September 18th 2013

Prediction Isn’t Just About Stocks. Predictive Persuasion


I think you'll find this Forbes blog post by David Leinweber of interest!:

Prediction Isn't Just About Stocks. Predictive Persuasion

David Leinweber, Contributor

Prediction isn’t just for the stock market. Trading is just one of many ways to cash in on quantitative foresight. For mass marketing – and even presidential campaigns – it’s another story. In those areas, putting odds on the future generates a different kind of power: the power to influence and persuade people – the power to not only predict but to actually change the future.

Persuasion by way of prediction is a whole other side to the big data world.

Predictive persuasion has a nice ring to it… and I bet you’ll never guess exactly what it is that companies predict in order to persuade. There’s a surprise twist in how it works. You already know that the whole point of marketing a product (or political candidate) is to influence consumers. But most don’t know that the new trend to do so goes beyond predicting consumer (and voter) behavior. Instead, those in power secure their lead by predicting how to best convince you. They increase their influence by predicting not your behavior, but how to influence your behavior. This technique is the bottom line in “mathematical seduction.” Predictive technology hasn’t only advanced and become more precise – it just got under your skin.

You’ve probably heard of the classic 1936 book, “How to Win Friends and Influence People.”  Perhaps the modern master of turning these ideas into science and software is the current occupant of 1600 Pennsylvania Avenue, who efforts in this direction were described in remarkable detail in a recent NY Times Magazine feature.

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September 16th 2013

Siri, You Can Drive My Car

big think

This transcript was originally published in Big Think. The article relates to my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

Siri, You Can Drive My Car

The main thing that’s going to happen with predictive analytics is that it’s just going to become more pervasive.  It’s going to be more ingrained.  It’s going to be used more and more.  We’re already being predicted all the time.  It’s influencing our experience – it’s hard to know how much.

I like to look at the year 2020 and say, “Well, what’s gonna happen when you’re driving to work?”  So one of the things that’s happening now is that your Smartphone is being more integrated with your car.

It either has its own integration to the cell network or you’re just docking your Smartphone. Either way you’re on the Web when you’re driving.  You’re connected.  You’re connected to the Cloud.  You’re connected to the ability to predict.  And this is going to actually affect a whole bunch of things, even just in your first hour of the day commuting to work in a car.  So you try to start the car and it takes some biometric readings and it says, “Hey, that’s not really you.” And it won’t let you start the car to prevent theft of the car.  It gives you recommendations of where to go grab breakfast.  It’s recommending restaurants knowing that you’re going to turn off the recommendations if you don’t like them.  It’s going to reroute your drive based on predictions.  Not just current traffic conditions but predictions of traffic to come – and use that to reroute the way you’re commuting to work.

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September 9th 2013

Review of “Predictive Analytics” by Stephen Few

Perceptual Edge

I was honored to have my book,  Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die reviewed by Stephen Few — check it out

Predictive Analytics — Eric Siegel Lights the Way

Predictive analytics is one of the most popular IT terms of our day, and like the others (Big Data, Data Science, etc.), it’s often defined far too loosely. People who work in the field of predictive analytics, however, use the term fairly precisely and meaningfully. No one, in my experience, does a better job of explaining predictive analytics—what it is, how it works, and why it’s important—than Eric Siegel, the founder of Predictive Analytics World, Executive Editor of the Predictive Analytics Times, and author of the new best-selling book in the field, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

Predictive analytics is a computer-based application of statistics that has grown out of an academic discipline that is traditionally called machine learning. Yes, even though computers can’t think, they can learn (i.e., acquire useful knowledge from data). Siegel defines predictive analytics as “technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.” (p. 11)

I appreciate the fact that Siegel doesn’t gush about the wonders of data and technology to the hyperbolic degree that is common today; he keeps a level head as he describes what can be done in realistic and practical terms. Here’s what he says about data:

As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein.(p. 4)

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September 3rd 2013

The Computer Knows Who You Are

Market Watch


I originally published this article in WSJ The article relates to my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

The Computer Knows Who You Are

Commentary: Peril, promise and the price of predictive technology

As computers are entrusted to make judgment calls traditionally decided by people, should we worry?

There’s a surprising twist. While some question whether the prescient machines that drive decisions by way of induction and prediction are trustworthy, an emerging problem is that they often predict too well. Predictive technology is so powerful, it reveals a future often considered private.

Millions of operational decisions in finance, marketing, law enforcement, and health care are now machine-driven — often with improved dexterity — using electronic predictions of human behavior, one person at a time. The technology to do this, predictive analytics, is a booming practice that’s taken hold across many industries.

Computerized prediction will never be perfect — like people, prognostic technology often gets it wrong, although in many applications it turns out to be more accurate than people are. But predictive analytics can cause difficulties not only when its predictions are wrong, but when its predictions are right.

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