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Tuesday, July 14, 2015

Data Analytics and How We Think.


I found this interesting syndicated article "Algorithms may echo human bias, study finds", on Today 14-July-2015, page 36. 

Basically it says that eventually, algorithms are created by humans, together with the human influences and biases. In other words, data analytics algorithms are merely human attempts to model a scenario mathematically with the help of very large amounts of data.

For instance, by applying graph theory on a social network platform, we can assign weightings on links to friends that have common interests with us and find who our closest friends are, who our best friends are or even who our spouse is. In plain language, we are looking for 'birds of a feather that flock together'.

There is also an algorithm that detects expense claim fraud, that analyses the first digit of each expense claim item. So if only a few  digit values are used and very repeatedy so, the expense claimant is flagged for further investigation. This probably based on the tendency that human beings will not think of  broad ranges of numbers when cheating. 

I trust that algorithms for data analytics have a symbiotic relationship with human psychology. So, it pays to observe patterns of human thinking through the data they manifest. May be some old proverbs may offer inspiration.


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Algorithms may echo human bias, study finds 

NEW YORK — There is a widespread belief that software and algorithms that rely on data are objective. But software is not free of human influence. Algorithms are written and maintained by people, and machinelearning algorithms adjust what they do based on people’s behaviour. As a result, algorithms can reinforce human prejudices, researchers say. 

A new study by Carnegie Mellon University researchers revealed that Google’s online advertising system showed an ad for high-income jobs to men much more often than women. Research from the University of Washington also found that a Google Images search for “CEO” produced 11 per cent women, even though 27 per cent of chief executives in the United States are women. 

Algorithms, which are instructions written by programmers, are often described as a black box; it is hard to know why websites produce certain results. Often, algorithms and online results reflect people’s attitudes and behaviour. The autocomplete feature on Google is an example — a recent search for “Are transgender” suggested, “Are transgenders going to hell”. 

“Even if they are not designed with the intent of discriminating against those groups, if they reproduce social preferences even in a completely rational way, they also reproduce those forms of discrimination,” said Mr David Oppenheimer, who teaches discrimination law at the University of California, Berkeley. 

The Carnegie Mellon researchers built a tool to simulate Google users who started with no search history, and then visited employment websites. Later, on a third-party news site, Google showed an ad for a career-coaching service advertising “US$200k+” executive positions 1,852 times to men and 318 times to women. The reason for the difference is unclear. It could have been that the advertiser requested that the ads be targeted towards men, or that the algorithm determined that men were more likely to click on the ads. 

Google declined to say how the ad showed up, but said: “Advertisers can choose to target the audience they want to reach, and we have policies that guide the type of interest-based ads that are allowed.” The New York Times

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