Month: September 2016

Romania players change squad numbers for maths problems before Spain game

In preparation for their friendly match with Spain, Romanias players have swapped their normal squad numbers for problems in a bid to help their countrys children with their maths

In preparation for their friendly match with Spain on Sunday night, Romanias players have swapped their normal squad numbers for maths problems in a bid to help their countrys children with their education.

The idea for the new numbers, printed on the back of training tops, will be supported by a video at the Cluj Arena explaining the initiative, which is designed to combat Romanian children dropping out of school as of 2014 the rate is 18%, one of the worst records in the European Union.

Football and mathematics are not mutually exclusive, said the Romanian Football Federation president, Razvan Burleanu. We must look at sports and education as not only complementary but fundamental elements integrated in the training and perfection of children. We want to have healthy generation and smart students who achieve performance and tools through tailored passions. Through this project, children will learn the basics of football and have an opportunity for the first time in our country to discover mathematics through an attractive approach.

Romania host Spain off the back of a 1-0 win over Lithuania on Wednesday. Having finished second in their Euro 2016 qualifying group behind Northern Ireland, they will play France in the opening match of the tournament on 10 June before going on to face Switzerland and Albania in the group stage.

Read more: http://www.theguardian.com/football/2016/mar/27/romania-players-change-squad-numbers-for-maths-equations-before-spain-game

Can you solve it? Are you smarter than the Gogglebox brainbox?

Bill from the telly will boggle your noddle

Hello guzzlers,

In the the week that Gogglebox is back on the telly, were all going to try our hands at some brilliant puzzles.

The following four gems were all devised by Bill from Gogglebox: hes the one that sits next to his pal Josef in a house in Cambridge.

Bill is William Hartston, a former British chess champion, a writer and a longtime lover of maths and puzzles. A kindred spirit.

To solve these brainteasers you will have to think laterally. If you are struggling Ill be back at noon UK with some tips. (Tips now added below)

Now relax on your sofa, make sure you have refreshment at hand a plate of biscuits, a Pot Noodle or a gin and tonic and enjoy:

1) What is the next number in the following series?

23, 9, 20, 14, 14, 9, 20, 6, …

2) Mary I; George III, Henry III, James II, George IV, Charles I, …

Why might Henry I be an appropriate way to end the series?

3) What comes next in the following series?

2.1, 3.5, 3.3, 2.3, 1.3, 2.4, 2.5, 2.6, 1.8 …

4) What comes next in this series:

1, 2, 9, 12, 70, 89, 97, 102 …

Thanks so much to Bill for letting me use these puzzles. His most recent book Even More Things That Nobody Knows: 501 Further Mysteries of Life, the Universe and Everything is terrific and is out in paperback in November.

Bill
Bill in his chess glory days Photograph: Bill Hartston

Ill be back with the answers at 5pm UK.

I post a puzzle here on a Monday every two weeks. If you want to propose a puzzle for this column, please email me Id love to hear it.

Im the author of several books on maths, as well as the kids book Football School: Where Football Explains the World which tells you loads of amazing stuff parents dont tell you such as when exactly footballers poo, why eagles are the most common mascot for football teams and how to play football on Mars

You can check me out on Twitter, Facebook, Google+, my personal website or my Guardian maths blog.

TIPS

1) Think about the alphabet.

2) Think what the numbers might be referring to.

3) Think about your keyboard.

4) Think about subtracting 1.

Read more: https://www.theguardian.com/science/2016/sep/26/can-you-solve-it-are-you-smarter-than-the-gogglebox-brainbox

Did you solve it? The logic question almost everyone gets wrong

The results are in and yes, most of you got this one wrong. Heres why.

Earlier today I set you this puzzle:

Jack is looking at Anne, but Anne is looking at George. Jack is married, but George is not. Is a married person looking at an unmarried person?

  • A: Yes
  • B: No
  • C: Cannot be determined

The correct answer is A.

Before I get to the explanation, a few words on why I set the question. I wanted to test if it really was the case that more than 80 per cent of people choose C. Well, the results are in, and with more than 200,000 submissions, this is how you voted:

  • A 27.68 per cent
  • B 4.55 per cent
  • C 67.77 per cent

More than 72 per cent of you chose the wrong answer. Maybe its an exaggeration to say that almost everyone gets this question wrong but the vast majority of you did! (And thats not accounting for the fact that many of you who took part are seasoned readers of this puzzle column, and were warned that this question was not all it seemed.)

Why is this question so tricky? It is because it appears to give you insufficient information. Annes marital status is not known, nor can it be determined, and so you make the inference that the question posed cannot be determined.

In fact, Annes marital status is irrelevant to the answer. If she is married, then a married person is looking at an unmarried person (Anne is looking at George), and if she isnt, a married person is looking at an unmarried person (Jack is looking at Anne).

Written down it becomes more obvious. If > means looking at then:

Jack > Anne > George, or

Married > Unknown > Unmarried

Replace Unkown with Married or with Unmarried and either way there is clearly a married person looking at an unmarried one.

This image may be helpful:

Pogo (@pogobeta) March 28, 2016

@alexbellos @bwecht pic.twitter.com/alptfqJePx

The puzzle caused many hands to be slapped on many foreheads.

Alex Rose (@Owlex_R) March 28, 2016

@alexbellos oh god, I’m so annoyed. Sorry, put me down in the “didn’t think hard enough and got it wrong” column.

As I expected, some people blamed getting the answer wrong on the poor wording of the question. For those of you who thought that Anne was not a person, then yes, C is the correct answer. But, come on guys, we can assume that Anne is a human being.

Robert Munafo (@mrob_27) March 28, 2016

@ShemyDjent @Hey_its_Boon @alexbellos @bwecht The question’s flaw is in using the word “person”; I must consider: what if Anne is my cat?

Others said that married and unmarried are not binary states, since what about widowed, or divorced. The Wikipedia article on marital status clears that one up.

Todays puzzle really belongs more to psychology than it does to mathematics or logic, as it is about the lazy assumptions we make, rather than whether or not we have the ability to solve the question.

Yet the reasoning that is used – that in order to solve something we need to consider all possibilities without knowing which is true – is frequently used in maths. In this video, the brilliant James Grime gives an example using irrational numbers.

The example about irrational numbers starts at 5.51

Source of todays puzzle: Rational and Irrational Thought: The Thinking That IQ Tests Miss by Keith E Stanovich, Scientific American.

I post a puzzle here every second Monday. My most recent book is Snowflake Seashell Star, a colouring book of mathematical images for all ages. (In the US its title is Patterns of the Universe.)

You can check me out on Twitter, Facebook, Google+ and my personal website. And if know of any great puzzles that you would like me to set here, get in touch.

Read more: https://www.theguardian.com/science/2016/mar/28/did-you-solve-it-the-logic-question-almost-everyone-gets-wrong

Here’s What It Takes To Raise Seriously Smart Kids, According To A 45-Year-Long Study

The Study of Mathematically Precocious Youth (SMPY) is one of the more colorfully named scientific studies. Now on its 45th year, it tracked the careers and accomplishments of up to 5,000 individuals, starting from when they were children or teenagers. As detailed by Nature, it would go on to transform the way gifted children are both identified and nurtured by the US education system.

More than anything other longitudinal study, it arguably is the best source in the world for understanding how to make children grow up with some impressive intellectual heft. It has produced hundreds of academic studies, and in particular, it appears to know how to spot talent ripe for development in the science, technology, engineering and mathematics (STEM) fields.

Unsurprisingly, many of those in SMPY which is coordinated by Vanderbilt University have gone on to become high-profile scientists. So whats the secret to turning your kids into potential geniuses?

Well, it appears that, contrary to many other studies, SMPYs data seems to suggest that a lot of it is born and bred in youth, and that inherent intelligence beats repeated practice when it comes to becoming an expert in something. In fact, early cognitive ability has a greater effect on achievement than either continued practice or other factors like the familys socio-economic status.

This finding also runs against the grain of most Western educational ethoses, which prioritize improving the abilities of children who struggle in this regard rather than those who have potential to reach great heights. Essentially, SMPY finds that if youre smart, and you are identified as such and nurtured, you will make it.

As such, standardized testing was a common method used by the initiative to find intellectually potent kids. Along with the partnered program at Johns Hopkins Universitys (JHU) Center for Talented Youth, the program tended to admit those who scored in the top 1percent in their university entrance exams.

Alumni included Mark Zuckerberg, Lady Gaga, and Google co-founder Sergey Brin, along with pioneering mathematicians Terence Tao and Lenhard Ng. Whether we like it or not, these people really do control our society, says Jonathan Wai, a psychologist at the Duke University Talent Identification Program in Durham, North Carolina, and a collaborator with JHU, told Nature.

Standardized testing is used to find those with high potential. bibiphoto/Shutterstock

Initiatives like the SMPY have also been criticized for how it may be putting too much emphasis on the smartest kids. Some worry that those with slightly more limited potential may be ignored by such initiatives. Additionally, labelling kids as smart from an early age could undermine their willingness to learn.

Importantly, it has not been conclusively shown that theres just one single factor that will guarantee your child will grow up to be the next Richard Feynman or Rosalind Franklin. Many different studies trying to pick apart the varying influences of nature versus nurture seem to settle on the idea that its a bit of both genetics and their upbringing.

One suggests that parental love, in terms of being very supportive and cooperative with your child around the pre-school age, significantly boosts their brain growth rate. Another study strongly hints that complex tasks that get increasingly difficult over time are huge boons to neural connectivity and mental flexibility.

Interestingly, computer games of varying kinds are structured in this way, and an increasing body of evidence suggests that the occasional spurt of virtual roaming, puzzle solving, or competitive combat in video games may contribute towards improving cognitive functions in later life. Learning how to play a musical instrument and regularly reading booksis just as neurologically beneficial for adults as it is for children.

SMPY suggests it’s clear early on if children will rise to the top of their fields. Pressmaster/Shutterstock

[H/T: Nature]

Read more: http://www.iflscience.com/brain/heres-what-it-takes-to-raise-seriously-smart-kids-according-to-a-45yearlong-study/

27 podcasts to make you smarter

Want to feel like a genius? These mind-expanding podcasts will give you everything you need to do just that

As the cold rustle of conkers start to hit the pavements and a new generation of pencil cases bulge under the weight of novelty felt-tips, it must be the start of a new term. Sadly, we cant all be going back to school. But that doesnt mean we need to let our brains fester like an opened tub of yoghurt on a hot day. The audio wonderland of modern podcasts is ripe with all the insight, analysis, facts, statistics and research you need to at least blag a GCSE. Here is a rundown of some of the best podcasts to make you feel (if not actually sound) like a genius:

Geography

The weekly stories from This American Life often spread beyond traditional state borders their recent episodes from Greeces refugee camps were brilliant, with reports on romance, wild pigs, women doing their laundry in a baseball stadium locker room and what its like to live in a former psychiatric hospital. You can also trawl the archive for stories from Haiti, the lie that saved Brazils economy and even the odd look at Europe (yes, they too covered Brexit).

A weekly listen to From Our Own Correspondent will do more for your understanding of global news than my curriculum ever managed, while The Documentary podcast from BBC World Service can cover anything from protest in Putins Russia to Syrias secret library and incubator babies on display in Coney Island.

English

The New Yorker Fiction Podcast is like the greatest book group, English seminar and public lecture you never joined. Each month the magazines fiction editor, Deborah Treisman, invites a different New Yorker author to choose and read aloud a story from the magazines archive then discuss it. I started with AM Homes reading Shirley Jacksons short story The Lottery, set in a sinister village, and it haunts me still.

Is English language your bag? Then Helen Zaltzmans The Allusionist is the etymology party youve been waiting for. After all, who knew a simple word like please could have such completely different meanings and uses on either side of the Atlantic?

If you like your journalism lengthy, perhaps the Longform interviews with journalists such as Malcolm Gladwell and Kathryn Schulz, speechwriters like Jon Favreau who worked with President Obama and editors like David Remnick will tickle your pickle, or the Guardians own excellent Long Reads audio series. After all, readings for squares, I heard.

Science

There are some amazing science stories in the RadioLab archive, from a genuinely terrifying exploration of the howling, spit-thickening spread of rabies, to the woman put in a coma as an experiment and a look at the day the dinosaurs died.

You can go a long way at a dinner party with a couple of the BBCs Inside Science episodes under your belt, and first dates will whizz by with the science anecdotes afforded by the last season of Invisibilia. Not to mention the unending popularity of Prof Brian Cox and Robin Inces Infinite Monkey Cage.

History

If Melvyn Bragg had been my history teacher, rather than a woman who decorated her classroom with warnings about the dangers of ragwort, I may have ended up with quite a different degree. In Our Time will teach you more about world history than most museums and you dont even need to pack a lunch. Im particularly enjoying the latest series on the history of The North.

Elsewhere, Stuff You Missed in History Class will give you a good grounding in everything from Chinas Great Leap Forward to the Matchgirls Strike and the Anglo-Cherokee war.

For those who prefer their history a little more specific, Im a huge fan of Great Lives, in which a notable modern figure looks at a person from the past who has inspired or excited them try Sara Pascoe on Virginia Woolf or Anthony Horowitz on Alfred Hitchcock.

Art

If The Essay from BBC Radio 3 was a country, Id move there. There are amazing features on everything from British film comedians to Dadaism, great sonnets, and the artistic impact of the fall of the Berlin Wall. Adventures in Design is a topical (if now behind-paywall) look at modern graphic design, while Song Exploder invites musicians and songwriters to pick apart their compositions in forensic detail. And if you want to delve into the world buildings-first, then 99% Invisible is far more than just an architecture podcast.

Sport

What I know about sport could fill the sweaty confines of a Slazenger Classic Abdo Guard, but even I have been known to laugh at the ArseBlog podcast. Particularly when Irish writer and presenter, Andrew Mangan, described striker Olivier Giroud as a big fucking ride.

Maths

The murky subjects of toxic debt, trading in oil, the economy of housing, Brexit and how to count your bitcoins are made more comprehensible thanks to NPRs Planet Money. It wont teach you how to do long division, but it will give you some prime number chat. Also worth a listen is the BBCs mathematics show More or Less, hosted by the Undercover Economist Tim Harford, which takes a look at the real stories behind the statistics found in the news.

General studies

Whether youre dating, starting a new job, or just want something new to say to your pets, you can learn a lot from the The Inquiry, which has programmes on everything from coral reefs to putting solar panels in the Sahara desert. The Middle East Week podcast will give you a great grounding on some of the worlds most controversial issues, More Perfect is a fascinating look at the American justice system and you can get to grips with some serious international relations via Global Dispatches. Little Atoms, from Resonance FM, will help you scrub up on politics, literature, science, art and comedy, and I have learned pretty much everything I know about farming, food, technology, growing vegetables and the rural economy thanks to The Archers (not to mention the rigours of baking ginger scones).

Read more: https://www.theguardian.com/tv-and-radio/2016/sep/07/27-podcasts-to-make-you-smarter

Twelve-year-old becomes Ivy League university’s youngest ever student

Jeremy Shuler, who was home-schooled by his aerospace engineer parents, is settling in at Cornell University and finding classes kind of easy so far

When he was two, Jeremy Shuler was reading books in English and Korean. At six, he was studying calculus. Now, at an age when most children are attending middle school, the exuberant 12-year-old is a freshman at Cornell University, the youngest the Ivy League school has on record.

Its risky to extrapolate, but if you look at his trajectory and he stays on course, one day hell solve some problem we havent even conceived of, said Cornell engineering dean Lance Collins. Thats pretty exciting.

Jeremy is the home-schooled child of two aerospace engineers who were living in Grand Prairie, Texas, when he applied to Cornell. While Jeremys elite-level SAT and advanced placement test scores in math and science at age 10 showed he was intellectually ready for college, Collins said what sealed the deal was his parents willingness to move to Ithaca. Jeremys father, Andy Shuler, transferred from Lockheed Martin in Texas to its location in upstate New York.

I wanted to make sure he had a nice, safe environment in terms of growing up, Collins said.

With his bowl-cut hair and frequent happy laughter, Jeremy is clearly still a child despite his advanced intelligence. He swung in his chair while his parents, who he calls Mommy and Daddy, recounted his early years during an interview at the engineering school where his grandfather is a professor, his father got his doctorate and Jeremy is now an undergraduate.

Jeremy
Jeremy Shuler, 12, with his parents Andy and Harrey. Photograph: Mike Groll/AP

From the beginning, he was physically advanced, very strong, said Harrey Shuler, who has a doctorate in aerospace engineering but put her career on hold to home-school Jeremy. He fixated on letters and numbers at three months old, knew the alphabet at 15 months, and was reading books on his own at 21 months in English and Korean, his mothers native language.

When he was five, he read The Lord of the Rings and Journey Through Genius: The Great Theorems of Mathematics on his own. Enrolling him in kindergarten was pointless.

We were concerned about him socialising with other kids, his mother said. At the playground he was freaked out by other kids running around screaming. But when we took him to Math Circle and math camp, he was very social. He needed someone with similar interests.

Jeremy nodded vehemently at that, saying his closest friends are from the math discussion groups. One of my Math Circle friends actually wrote Minecraft for Dummies, he said, adding that the computer game is one of his favourite pastimes, along with reading science fiction.

He said hes settling in to college life.

I was nervous at first, but Im a lot more excited than nervous now, he said, adding that hes already made a couple of friends. As Mommy said, all the kids in math camp were older than me, so Im used to having older friends. As long as they like math.

Hes enjoying the classes, especially the theoretical discussions, he said. The classes are kind of easy so far, but I know theyll be harder pretty soon.

Thats an important thing to keep in mind, according to others with experience in early college. Joe Bates, founder of Singular Computing in Newton, Massachusetts, and a leading researcher in artificial intelligence, entered Johns Hopkins University when he was 13. Now 60, Bates said college was liberating after conventional schooling that always bored him.

Jeremy
Jeremy Shuler on campus in Ithaca. Photograph: Mike Groll/AP

It was actually the first time it was fun and interesting to be in school, Bates said. On a social level, he felt more at home with his nerdy college classmates than he had with junior high students.

If I were to give Jeremy any advice, it would be that it can be hard and you should not assume you can manage everything, Bates said, recalling how distressed he was when he found himself struggling with his doctoral studies at Cornell engineering.

You should truly keep your parents and advisers informed, and ask them for help. Its not going to be like before, when you could just do everything.

As for the future, Jeremy plans to just keep on learning. I want to pursue a career in academia, he said.

Read more: https://www.theguardian.com/us-news/2016/sep/02/twelve-year-old-ivy-league-university-youngest-student

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How algorithms rule our working lives | Cathy ONeil

The Long Read: Employers are turning to mathematically modelled ways of sifting through job applications. Even when wrong, their verdicts seem beyond dispute and they tend to punish the poor

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A few years ago, a young man named Kyle Behm took a leave from his studies at Vanderbilt University in Nashville, Tennessee. He was suffering from bipolar disorder and needed time to get treatment. A year and a half later, Kyle was healthy enough to return to his studies at a different university. Around that time, he learned from a friend about a part-time job. It was just a minimum-wage job at a Kroger supermarket, but it seemed like a sure thing. His friend, who was leaving the job, could vouch for him. For a high-achieving student like Kyle, the application looked like a formality.

But Kyle didnt get called in for an interview. When he inquired, his friend explained to him that he had been red-lighted by the personality test hed taken when he applied for the job. The test was part of an employee selection program developed by Kronos, a workforce management company based outside Boston. When Kyle told his father, Roland, an attorney, what had happened, his father asked him what kind of questions had appeared on the test. Kyle said that they were very much like the five factor model test, which hed been given at the hospital. That test grades people for extraversion, agreeableness, conscientiousness, neuroticism, and openness to ideas.

At first, losing one minimum-wage job because of a questionable test didnt seem like such a big deal. Roland Behm urged his son to apply elsewhere. But Kyle came back each time with the same news. The companies he was applying to were all using the same test, and he wasnt getting offers.

Roland Behm was bewildered. Questions about mental health appeared to be blackballing his son from the job market. He decided to look into it and soon learned that the use of personality tests for hiring was indeed widespread among large corporations. And yet he found very few legal challenges to this practice. As he explained to me, people who apply for a job and are red-lighted rarely learn that they were rejected because of their test results. Even when they do, theyre not likely to contact a lawyer.

Behm went on to send notices to seven companies, including Home Depot and Walgreens, informing them of his intent to file a class-action suit alleging that the use of the exam during the job application process was unlawful. The suit, as I write this, is still pending. Arguments are likely to focus on whether the Kronos test can be considered a medical exam, the use of which in hiring is illegal under the Americans with Disabilities Act of 1990. If this turns out to be the case, the court will have to determine whether the hiring companies themselves are responsible for running afoul of the ADA, or if Kronos is.

But the questions raised by this case go far beyond which particular company may or may not be responsible. Automatic systems based on complicated mathematical formulas, such as the one used to sift through Behms job application, are becoming more common across the developed world. And given their scale and importance, combined with their secrecy, these algorithms have the potential to create an underclass of people who will find themselves increasingly and inexplicably shut out from normal life.


It didnt have to be this way. After the financial crash, it became clear that the housing crisis and the collapse of major financial institutions had been aided and abetted by mathematicians wielding magic formulas. If we had been clear-headed, we would have taken a step back at this point to figure out how we could prevent a similar catastrophe in the future. But instead, in the wake of the crisis, new mathematical techniques were hotter than ever, and expanding into still more domains. They churned 24/7 through petabytes of information, much of it scraped from social media or e-commerce websites. And increasingly they focused not on the movements of global financial markets but on human beings, on us. Mathematicians and statisticians were studying our desires, movements, and spending patterns. They were predicting our trustworthiness and calculating our potential as students, workers, lovers, criminals.

This was the big data economy, and it promised spectacular gains. A computer program could speed through thousands of rsums or loan applications in a second or two and sort them into neat lists, with the most promising candidates on top. This not only saved time but also was marketed as fair and objective. After all, it didnt involve prejudiced humans digging through reams of paper, just machines processing cold numbers. By 2010 or so, mathematics was asserting itself as never before in human affairs, and the public largely welcomed it.

Most of these algorithmic applications were created with good intentions. The goal was to replace subjective judgments with objective measurements in any number of fields whether it was a way to locate the worst-performing teachers in a school or to estimate the chances that a prisoner would return to jail.

These algorithmic solutions are targeted at genuine problems. School principals cannot be relied upon to consistently flag problematic teachers, because those teachers are also often their friends. And judges are only human, and being human they have prejudices that prevent them from being entirely fair their rulings have been shown to be harsher right before lunch, when theyre hungry, for example so its a worthy goal to increase consistency, especially if you can rest assured that the newer system is also scientifically sound.

The difficulty is that last part. Few of the algorithms and scoring systems have been vetted with scientific rigour, and there are good reasons to suspect they wouldnt pass such tests. For instance, automated teacher assessments can vary widely from year to year, putting their accuracy in question. Tim Clifford, a New York City middle school English teacher of 26 years, got a 6 out of 100 in one year and a 96 the next, without changing his teaching style. Of course, if the scores didnt matter, that would be one thing, but sometimes the consequences are dire, leading to teachers being fired.

There are also reasons to worry about scoring criminal defendants rather than relying on a judges discretion. Consider the data pouring into the algorithms. In part, it comes from police interactions with the populace, which is known to be uneven, often race-based. The other kind of input, usually a questionnaire, is also troublesome. Some of them even ask defendants if their families have a history of being in trouble with the law, which would be unconstitutional if asked in open court but gets embedded in the defendants score and labelled objective.

It doesnt stop there. Algorithms are being used to determine how much we pay for insurance (more if your credit score is low, even if your driving record is clean), or what the terms of our loans will be, or what kind of political messaging well receive. There are algorithms that find out the weather forecast and only then decide on the work schedule of thousands of people, laying waste to their ability to plan for childcare and schooling, never mind a second job.

Their popularity relies on the notion they are objective, but the algorithms that power the data economy are based on choices made by fallible human beings. And, while some of them were made with good intentions, the algorithms encode human prejudice, misunderstanding, and bias into automatic systems that increasingly manage our lives. Like gods, these mathematical models are opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, are beyond dispute or appeal. And they tend to punish the poor and the oppressed in our society, while making the rich richer. Thats what Kyle Behm learned the hard way.


Finding work used to be largely a question of whom you knew. In fact, Kyle Behm was following the traditional route when he applied for work at Kroger. His friend had alerted him to the opening and put in a good word. For decades, that was how people got a foot in the door, whether at grocers, banks, or law firms. Candidates then usually faced an interview, where a manager would try to get a feel for them. All too often this translated into a single basic judgment: is this person like me (or others I get along with)? The result was a lack of opportunity for job seekers without a friend inside, especially if they came from a different race, ethnic group, or religion. Women also found themselves excluded by this insider game.

Companies like Kronos brought science into corporate human resources in part to make the process fairer. Founded in the 1970s by MIT graduates, Kronoss first product was a new kind of punch clock, one equipped with a microprocessor, which added up employees hours and reported them automatically. This may sound banal, but it was the beginning of the electronic push now blazing along at warp speed to track and optimise a workforce.

As Kronos grew, it developed a broad range of software tools for workforce management, including a software program, Workforce Ready HR, that promised to eliminate the guesswork in hiring. According to its web page, Kronos can help you screen, hire, and onboard candidates most likely to be productive the best-fit employees who will perform better and stay on the job longer.

Kronos is part of a growing industry. The hiring business is becoming automated, and many of the new programs include personality tests like the one Kyle Behm took. It is now a $500 million annual business and is growing by 10 to 15% a year, according to Hogan Assessment Systems Inc, a company that develops online personality tests. Such tests now are used on 60 to 70% of prospective workers in the US, and in the UK, according to the Association of Graduate Recruiters, 71% of employers use some form of psychometric test for recruitment.

Even putting aside the issues of fairness and legality, research suggests that personality tests are poor predictors of job performance. Frank Schmidt, a business professor at the University of Iowa, analysed a century of workplace productivity data to measure the predictive value of various selection processes. Personality tests ranked low on the scale they were only one-third as predictive as cognitive exams, and also far below reference checks. The primary purpose of the test, said Roland Behm, is not to find the best employee. Its to exclude as many people as possible as cheaply as possible.


You might think that personality tests would be easy to game. If you go online to take a five factor personality test, it looks like a cinch. One question asks: Have frequent mood swings? It would probably be smart to answer very inaccurate. Another asks: Get mad easily? Again, check no.

In fact, companies can get in trouble for screening out applicants on the basis of such questions. Regulators in Rhode Island found that CVS Pharmacy was illegally screening out applicants with mental illnesses when a personality test required respondents to agree or disagree with such statements as People do a lot of things that make you angry and Theres no use having close friends; they always let you down.

Illustration
Illustration by Nathalie Lees

More intricate questions, which are harder to game, are more likely to keep the companies out of trouble. Consequently, many of the tests used today force applicants to make difficult choices, likely to leave them with a sinking feeling of Damned if I do, damned if I dont.

McDonalds, for example, recently asked prospective workers to choose which of the following best described them: It is difficult to be cheerful when there are many problems to take care of or Sometimes, I need a push to get started on my work.

In 2014, the Wall Street Journal asked a psychologist who studies behaviour in the workplace, Tomas Chamorro-Premuzic, to analyse thorny questions like these. The first of the two answers to the question from McDonalds, Chamorro-Premuzic said, captured individual differences in neuroticism and conscientiousness; the second, low ambition and drive. So the prospective worker is pleading guilty to being either high-strung or lazy.

A Kroger supermarket question was far simpler: Which adjective best describes you at work, unique or orderly? Answering unique, said Chamorro-Premuzic, captures high self-concept, openness and narcissism, while orderly expresses conscientiousness and self-control.

Note that theres no option to answer all of the above. Prospective workers must pick one option, without a clue as to how the program will interpret it. And some of the analysis will draw unflattering conclusions.

Defenders of the tests note that they feature lots of questions and that no single answer can disqualify an applicant. Certain patterns of answers, however, can and do disqualify them. And we do not know what those patterns are. Were not told what the tests are looking for. The process is entirely opaque.

Whats worse, after the model is calibrated by technical experts, it receives precious little feedback. Sports provide a good contrast here. Most professional basketball teams employ data geeks, who run models that analyse players by a series of metrics, including foot speed, vertical leap, free-throw percentage, and a host of other variables. Teams rely on these models when deciding whether or not to recruit players. But if, say, the Los Angeles Lakers decide to pass on a player because his stats suggest that he wont succeed, and then that player subsequently becomes a star, the Lakers can return to their model to see what they got wrong. Whatever the case, they can work to improve their model.

Now imagine that Kyle Behm, after getting red-lighted at Kroger, goes on to land a job at McDonalds. He turns into a stellar employee. Hes managing the kitchen within four months and the entire franchise a year later. Will anyone at Kroger go back to the personality test and investigate how they could have got it so wrong?

Not a chance, Id say. The difference is this: Basketball teams are managing individuals, each one potentially worth millions of dollars. Their analytics engines are crucial to their competitive advantage, and they are hungry for data. Without constant feedback, their systems grow outdated and dumb. The companies hiring minimum-wage workers, by contrast, act as if they are managing herds. They slash expenses by replacing human resources professionals with machines, and those machines filter large populations into more manageable groups. Unless something goes haywire in the workforce an outbreak of kleptomania, say, or plummeting productivity the company has little reason to tweak the filtering model. Its doing its job even if it misses out on potential stars. The company may be satisfied with the status quo, but the victims of its automatic systems suffer.


The majority of job applicants, thankfully, are not blackballed by automatic systems. But they still face the challenge of moving their application to the top of the pile and landing an interview. This has long been a problem for racial and ethnic minorities, as well as women.

The ideal way to circumvent such prejudice is to consider applicants blindly. Orchestras, which had long been dominated by men, famously started in the 1970s to hold auditions with the musician hidden behind a sheet. Connections, reputations, race or alma mater no longer mattered. The music from behind the sheet spoke for itself. Since then, the percentage of women playing in major orchestras has leapt by a factor of five though they still make up only a quarter of the musicians.

The trouble is that few professions can engineer such an evenhanded tryout for job applicants. Musicians behind the sheet can actually perform the job theyre applying for, whether its a Dvok cello concerto or bossa nova on guitar. In other professions, employers have to hunt through CVs, looking for qualities that might predict success.

As you might expect, human resources departments rely on automatic systems to winnow down piles of rsums. In fact, in the US, some 72% of CVs are never seen by human eyes. Computer programs flip through them, pulling out the skills and experiences that the employer is looking for. Then they score each CV as a match for the job opening. Its up to the people in the human resources department to decide where the cutoff is, but the more candidates they can eliminate with this first screening, the fewer human hours theyll have to spend processing the top matches.

So job applicants must craft their rsums with that automatic reader in mind. Its important, for example, to sprinkle the rsum liberally with words the specific job opening is looking for. This could include previous positions (sales manager, software architect), languages (Mandarin, Java), or honours (summa cum laude). Those with the latest information learn what machines appreciate and what tangles them up, and tailor their applications accordingly.

The result of these programs is that those with the money and resources to prepare their rsums come out on top. Those who dont take these steps may never know that theyre sending their rsums into a black hole. Its one more example in which the wealthy and informed get the edge and the poor are more likely to lose out.


So far, weve been looking at models that filter out job candidates. For most companies, those models are designed to cut administrative costs and to reduce the risk of bad hires (or ones that might require more training). The objective of the filters, in short, is to save money.

HR departments, of course, are also eager to save money through the hiring choices they make. One of the biggest expenses for a company is workforce turnover, commonly called churn. Replacing a worker earning $50,000 a year costs a company about $10,000, or 20% of that workers yearly pay, according to the Center for American Progress. Replacing a high-level employee can cost as much as two years of salary.

Naturally, many hiring models attempt to calculate the likelihood that a job candidate will stick around. Evolv, Inc, now a part of Cornerstone OnDemand, helped Xerox scout out prospects for its call centres, which employ more than 40,000 people. The churn model took into account some of the metrics you might expect, including the average time people stuck around on previous jobs. But they also found some intriguing correlations. People the system classified as creative types tended to stay longer at the job, while those who scored high on inquisitiveness were more likely to set their questioning minds towards other opportunities.

But the most problematic correlation had to do with geography. Job applicants who lived farther from the job were more likely to churn. This makes sense: long commutes are a pain. But Xerox managers noticed another correlation. Many of the people suffering those long commutes were coming from poor neighbourhoods. So Xerox, to its credit, removed that highly correlated churn data from its model. The company sacrificed a bit of efficiency for fairness.

While churn analysis focuses on the candidates most likely to fail, the more strategically vital job for HR departments is to locate future stars, the people whose intelligence, inventiveness, and drive can change the course of an entire enterprise. In the higher echelons of the economy, companies are on the hunt for employees who think creatively and work well in teams. So the modellers challenge is to pinpoint, in the vast world of big data, the bits of information that correlate with originality and social skills.

A pioneer in this field is Gild, a San Franciscobased startup. Extending far beyond a prospects alma mater or rsum, Gild sorts through millions of job sites, analysing what it calls each persons social data. The company develops profiles of job candidates for its customers, mostly tech companies, keeping them up to date as the candidates add new skills. Gild claims that it can even predict when a star employee is likely to change jobs and can alert its customer companies when its the right time to make an offer.

But Gilds model attempts to quantify and also qualify each workers social capital. How integral is this person to the community of fellow programmers? Do they share and contribute code? Say a Brazilian coder Pedro, lets call him lives in So Paulo and spends every evening from dinner to one in the morning in communion with fellow coders the world over, solving cloud-computing problems or brainstorming gaming algorithms on sites such as GitHub or Stack Overflow. The model could attempt to gauge Pedros passion (which probably gets a high score) and his level of engagement with others. It would also evaluate the skill and social importance of his contacts. Those with larger followings would count for more. If his principal online contact happened to be Googles Sergey Brin, say, Pedros social score would no doubt shoot through the roof.

But models like Gilds rarely receive such explicit signals from the data. So they cast a wider net, in search of correlations to workplace stardom wherever they can find them. And with more than six million coders in their database, the company can find all kinds of patterns. Vivienne Ming, Gilds chief scientist, said in an interview with Atlantic Monthly that Gild had found a bevy of talent frequenting a certain Japanese manga site. If Pedro spends time at that comic-book site, of course, it doesnt predict superstardom. But it does nudge up his score.

That makes sense for Pedro. But certain workers might be doing something else offline, which even the most sophisticated algorithm couldnt infer at least not today. They might be taking care of children, for example, or perhaps attending a book group. The fact that prospects dont spend six hours discussing manga every evening shouldnt be counted against them. And if, like most of techdom, that manga site is dominated by males and has a sexist tone, a good number of the women in the industry will probably avoid it.

Despite these issues, Gilds category of predictive model has more to do with rewarding people than punishing them. It is tame compared with widely-used personality tests that exclude people from opportunities. Still, its important to note that these hiring models are ever-evolving. The world of data continues to expand, with each of us producing ever-growing streams of updates about our lives. All of this data will feed our potential employers insights into us.

Will those insights be tested, or simply used to justify the status quo and reinforce prejudices? When I consider the sloppy and self-serving ways that companies often use data, Im reminded of phrenology, a pseudoscience that was briefly popular in the 19th century. Phrenologists would run their fingers over the patients skull, probing for bumps and indentations. Each one, they thought, was linked to personality traits. If a patient was morbidly anxious or suffering from alcoholism, the skull probe would usually find bumps and dips that correlated with that observation which, in turn, bolstered faith in the science of phrenology.

Phrenology was a model that relied on pseudoscientific nonsense to make authoritative pronouncements, and for decades it went untested. Big data can fall into the same trap. Models like the ones that red-lighted Kyle Behm continue to lock people out, even when the science inside them is little more than a bundle of untested assumptions.

Mail illustration: Nathalie Lees

This essay is adapted from Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, published by Allen Lane on 6 September

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Read more: https://www.theguardian.com/science/2016/sep/01/how-algorithms-rule-our-working-lives