DiscriminAItion

Even if democracy manages to adapt and survive, people might become the victims of new kinds of oppression and discrimination. Already today more and more banks, corporations and institutions are using algorithms to analyse data and make decisions about us. When you apply to your bank for a loan, it is likely that your application is processed by an algorithm rather than by a human. The algorithm analyses lots of data about you and statistics about millions of other people, and decides whether you are reliable enough to give you a loan. Often, the algorithm does a better job than a human banker. But the problem is that if the algorithm discriminates against some people unjustly, it is difficult to know that. If the bank refuses to give you a loan, and you ask 'Why?', the bank replies, 'The algorithm said no.' You ask, 'Why did the algorithm say no? What's wrong with me?', and the bank replies, 'We don't know. No human understands this algorithm, because it is based on advanced machine learning. But we trust our algorithm, so we won't give you a loan.'

When discrimination is directed against entire groups, such as women or black people, these groups can organise and protest against their collective discrimination. But now an algorithm might discriminate against you personally, and you have no idea why. Maybe the algorithm found something in your DNA, your personal history or your Facebook account that it does not like. The algorithm discriminates against you not because you are a woman, or an African American — but because you are you. There is something specific about you that the algorithm does not like. You don't know what it is, and even if you knew, you cannot organise with other people to protest, because there are no other people suffering the exact same prejudice. It is just you. Instead of just collective discrimination, in the twenty-first century we might face a growing problem of individual discrimination.

Lies: About 1,160,000,000 results (0.51 seconds)

Frankly, the overwhelming majority of academics have ignored the data explosion caused by the digital age. The world’s most famous sex researchers stick with the tried and true. They ask a few hundred subjects about their desires; they don’t ask sites like PornHub for their data. The world’s most famous linguists analyze individual texts; they largely ignore the patterns revealed in billions of books. The methodologies taught to graduate students in psychology, political science, and sociology have been, for the most part, untouched by the digital revolution. The broad, mostly unexplored terrain opened by the data explosion has been left to a small number of forward-thinking professors, rebellious grad students, and hobbyists. That will change.

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Everybody lies. People lie about how many drinks they had on the way home. They lie about how often they go to the gym, how much those new shoes cost, whether they read that book. They call in sick when they’re not. They say they’ll be in touch when they won’t. They say it’s not about you when it is. They say they love you when they don’t. They say they’re happy while in the dumps. They say they like women when they really like men. People lie to friends. They lie to bosses. They lie to kids. They lie to parents. They lie to doctors. They lie to husbands. They lie to wives. They lie to themselves. And they damn sure lie to surveys. Here’s my brief survey for you:

Have you ever cheated in an exam?

Have you ever fantasised about killing someone?

Were you tempted to lie?

Many people underreport embarrassing behaviours and thoughts on surveys. They want to look good, even though most surveys are anonymous. This is called social desirability bias. 

An important paper in 1950 provided powerful evidence of how surveys can fall victim to such bias. Researchers collected data, from official sources, on the residents of Denver: what percentage of them voted, gave to charity, and owned a library card. They then surveyed the residents to see if the percentages would match. The results were, at the time, shocking. What the residents reported to the surveys was very different from the data the researchers had gathered. Even though nobody gave their names, people, in large numbers, exaggerated their voter registration status, voting behaviour, and charitable giving.

Has anything changed in 65 years? In the age of the internet, not owning a library card is no longer embarrassing. But, while what’s embarrassing or desirable may have changed, people’s tendency to deceive pollsters remains strong. A recent survey asked University of Maryland graduates various questions about their college experience. The answers were compared with official records. People consistently gave wrong information, in ways that made them look good. Fewer than 2% reported that they graduated with lower than a 2.5 GPA (grade point average). In reality, about 11% did. And 44% said they had donated to the university in the past year. In reality, about 28% did.

Then there’s that odd habit we sometimes have of lying to ourselves. Lying to oneself may explain why so many people say they are above average. How big is this problem? More than 40% of one company’s engineers said they are in the top 5%. More than 90% of college professors say they do above-average work. One-quarter of high school seniors think they are in the top 1% in their ability to get along with other people. If you are deluding yourself, you can’t be honest in a survey.

The more impersonal the conditions, the more honest people will be. For eliciting truthful answers, internet surveys are better than phone surveys, which are better than in-person surveys. People will admit more if they are alone than if others are in the room with them. However, on sensitive topics, every survey method will elicit substantial misreporting. People have no incentive to tell surveys the truth.

How, therefore, can we learn what our fellow humans are really thinking and doing? Big data. Certain online sources get people to admit things they would not admit anywhere else. They serve as a digital truth serum. Think of Google searches. Remember the conditions that make people more honest. Online? Check. Alone? Check. No person administering a survey? Check.

The power in Google data is that people tell the giant search engine things they might not tell anyone else. Google was invented so that people could learn about the world, not so researchers could learn about people, but it turns out the trails we leave as we seek knowledge on the internet are tremendously revealing.

I have spent the past four years analysing anonymous Google data. The revelations have kept coming. Mental illness, human sexuality, abortion, religion, health. Not exactly small topics, and this dataset, which didn’t exist a couple of decades ago, offered surprising new perspectives on all of them. I am now convinced that Google searches are the most important dataset ever collected on the human psyche.

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Reality generates data. Or is it the other way around?

Everyone knows that dragons don’t exist. But while this simplistic formulation may satisfy the layman, it does not suffice for the scientific mind. The School of Higher Neantical Nillity is in fact wholly unconcerned with what does exist. Indeed, the banality of existence has been so amply demonstrated, there is no need for us to discuss it any further here. The brilliant Cerebron, attacking the problem analytically, probability theory to this area and, in so doing, created the field of statistical draconics, which says that dragons are thermodynamically impossible only in the probabilistic sense, as are elves, fairies, gnomes, witches, pixies and the like. Using the general equation of improbability, the two constructors obtained the coefficients of pixation, elfinity, kobolding, etc. They found that for the spontaneous manifestation of an average dragon, one would have to wait a good sixteen quintoquadrillion heptillion years. In other words, the whole problem would have remained a mathematical curiosity had it not been for that famous tinkering passion of Trurl, who decided to examine the nonphenomenon empirically. First, as he was dealing with the highly improbable, he invented a probability amplifier and ran tests in his basement – then later at the Dracogenic Proving Grounds established and funded by the probability theory to this area and, in so doing, created the field of statistical draconics, which says that dragons are thermodynamically impossible only in the probabilistic sense, as are elves, fairies, gnomes, witches, pixies and the like. Using the general equation of improbability, the two constructors obtained the coefficients of pixation, elfinity, kobolding, etc. They found that for the spontaneous manifestation of an average dragon, one would have to wait a good sixteen quintoquadrillion heptillion years. In other words, the whole problem would have remained a mathematical curiosity had it not been for that famous tinkering passion of Trurl, who decided to examine the nonphenomenon empirically. First, as he was dealing with the highly improbable, he invented a probability amplifier and ran tests in his basement – then later at the Dracogenic Proving Grounds established and funded by the Academy. To this day those who (sadly enough) have no knowledge of the General Theory of Improbability ask why Trurl probabilized a dragon and not an elf or goblin. The answer is simply that dragons are more probable than elves or goblins to begin with. True, Trurl might have gone further with his amplifying experiments, had not the first been so discouraging – discouraging in that the materialized dragon tried to make a meal of him. Fortunately, Klapaucius was nearby and lowered the probability, and the monster vanished.

The great leap backwards.

In 1957, a billion Chinese were going hungry.
Mao Zedong couldn’t admit this was because of the failings of his communist agricultural policies.
The reason must be something else.
He heard that sparrows were eating lots of grain.
That must be the reason.
So began ‘The Great Sparrow Campaign’.
The people must do whatever was necessary to rid China of sparrows.
That way the people would have plenty to eat.
It became everyone’s responsibility to help wipe out sparrows.
Masses of schoolchildren were taken on outings to destroy nests, to smash eggs, to kill chicks.
Everyone with any kind of gun was told to shoot sparrows wherever they saw them.
Poison was put wherever sparrows lived.
The Chinese organised in thousands to visit the areas where the sparrows gathered.
They did anything to stop them landing in the trees.
They made vast amounts of noise: sounding horns, thumping drums, even banging old pots and pans.
Propaganda films of the period show entire villages participating right across China.
They wouldn’t let the sparrows land and eventually the sparrows exhausted themselves and dropped to earth dead.
All over China, towns and villages were given recognition for the amount of sparrows they killed.
One day alone, in Shanghai, they killed 198,000.
Eventually, sparrows in China were eradicated, around two billion birds.
So that was the end of the problem, now food would be plentiful.
Well not quite.
What Mao Zedong hadn’t allowed for was what else the sparrows ate, besides grain.
They ate locusts.
Without the sparrows, the locusts had nothing to stop them.
They multiplied on a massive scale.
And locusts were many times more destructive than sparrows.
Plagues of locusts took over huge areas of Chinese farmland.
Each swarm covering hundreds of square miles made up of trillions of locusts.
It resulted in the Great Famine.
Which resulted in thirty million people dead from starvation.
Which created a new problem: what could be done to control the locusts?
The only solution was for China to import millions of sparrows from Communist Russia.
To try to put everything back the way it had been.
Because the solution had been worse than the problem.
Which is pretty much what’s happened to advertising.
Advertising was good, but we were looking for a way to make it better.
So we had to replace intuition and normal common-sense.
We had to make everything rational and verifiable, measurable and accountable, sensible and scientific.
And what happened?
We killed off the intuitive, the common-sense, the fun.
Advertising became formulaic, dull, invisible and predictable.

We killed off the sparrows and the locusts were worse.

Dave Trott (2015) Dave Trott's Blog.