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Social rating and machine learning

February 18, 2017 By editor

I recently blogged about the risks of social rating systems. Machine learning adds another dimension to this.

In her book “Weapons of Math Destruction”, Cathy O’Neil highlights the umaccountability of algorithms used to make decisions that have a significant impact on peoples’ lives. The details of these algorithms are often undocumented—for commercial or security reasons—making it difficult to challenge their conclusions.

Unaccountable big data algorithms serve to amplify the risks posed by social scoring.

Filed Under: Big data, Machine learning Tagged With: algorithms, social scoring

Machine learning replacing traders at Goldman Sachs

February 16, 2017 By editor

trading dashboard

Traditionally automation has displaced low skilled labor. But the big opportunities for machine learning lie in the areas currently staffed by professionals.

Marty Chavez, deputy CFO at Goldman Sachs recently explained that automation had cost 600 equity traders their jobs. Furthermore, he went on to describe how the same innovation is being applied to areas such as currency trading and even some investment banking operations.

Goldman Sachs predict that they’ll be replacing traders with computer engineers at a ratio of 4:1. Out of the 9000 people employed at the bank around 3000 are engineers.

Filed Under: Machine learning Tagged With: automation, goldman sachs, jobs

Slot machine hack

February 16, 2017 By editor

slot machine

Wired has an article on a slot machine hack that seems to make use of data analytics.

The hack relies on a weakness introduced in the pseudo-random number generator used by the machines from Austrian gaming company Novomatic. Teams in the casino video the machines using their cellphones. The videos are then sent to St Petersburg where they are analyzed—there are no technical details available on the “back room” analysis techniques.

Once the analysis has been performed, the casino team receive a vibration via a custom phone app that prompts them to press the spin button in 0.25 seconds–the normal reaction time of a person.

It’s not a perfect method, but, statistically, it provides enough of an edge for the slot machine players to consistently come out ahead in the long term.

One fix would be to use a true random number generator, such as a device that exploits the true randomness of quantum physics. However, updating all the existing slot machines would clearly be expensive.

Filed Under: Data analysis Tagged With: gambling, hack, pseudo-random number generator, slot machine

Personal rating dystopias

February 16, 2017 By editor

Black Mirror’s “Nosedive” episode portrays a future society, frighteningly like our own, in which people rate each other as a consequence of all kinds of trivial social interactions. Your overall rating is public and determines your job prospects, housing options, social invitations, etc—causing people to obsess over improving them.

As in most public policy decisions you control behavior by tweaking the incentives.

This terrifies me—because I can see it happening. I’ve since been informed that Uber pretty much operates along similar lines.

Now, it has to be said, I’m not a huge fan of social media. And, I’ve railed against the pointless tyranny of personal ratings in the past. To say the least, the world portrayed in the show isn’t my kind of thing.

So, imagine my horror to read in the Wall Street Journal that

Beijing wants to give every citizen a score based on behavior such as spending habits, turnstile violations and filial piety, which can blacklist citizens from loans, jobs, air travel

My concern is that we know that data science is a bit of an art form. False positives appear all the time when profiling potential terrorists. Recommendation systems run the gamut from bloody obvious to downright bizarre. Many corporations can’t begin to make sense of their own data lakes. Basically, it’s a work in progress.

Yet, here we are…on the verge of disenfranchising people on the basis of scores that, I can guarantee you, will be fundamentally flawed.

Filed Under: Big data, Data analysis, Data science Tagged With: Black Mirror, idiosyncratic rater effect, Nosedive, personal ratings, ratings

Visualization pioneer, Hans Rosling, dies aged 68

February 9, 2017 By editor

Gapminder screenshot

Hans Rosling has died this week, aged 68. He become famous due to a TED talk he gave in 2006. In the talk he brought international health statistics to life via animated visualizations. He used motion to help viewers make sense of the data.

The software used in his talk was later released as Gapminder and can be used by anyone wishing to display their own data using time-based animation.

Filed Under: Data analysis Tagged With: Gapminder, Hans Rosling, TED, visualization

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