algorithms

Cross Post: Biased Algorithms: Here’s a More Radical Approach to Creating Fairness

Written by Dr Tom Douglas

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Our lives are increasingly affected by algorithms. People may be denied loans, jobs, insurance policies, or even parole on the basis of risk scores that they produce.

Yet algorithms are notoriously prone to biases. For example, algorithms used to assess the risk of criminal recidivism often have higher error rates in minority ethic groups. As ProPublica found, the COMPAS algorithm – widely used to predict re-offending in the US criminal justice system – had a higher false positive rate in black than in white people; black people were more likely to be wrongly predicted to re-offend.

Corrupt code.
Vintage Tone/Shutterstock

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Scrabbling for Augmentation

By Stephen Rainey

 

Around a decade ago, Facebook users were widely playing a game called ‘Scrabulous’ with one another. It was pretty close to Scrabble, effectively, leading to a few legal issues.

Alongside Scrabulous, the popularity of Scrabble-assistance websites grew. Looking over the shoulders of work colleagues, you could often spy a Scrabulous window, as well as one for scrabblesolver.co.uk too. The strange phenomenon of easy, online Scrabulous cheating seemed pervasive for a time.

The strangeness of this can hardly be overstated. Friends would be routinely trying to pretend to one another that they were superior wordsmiths, by each deploying algorithmic anagram solvers. The ‘players’ themselves would do nothing but input data to the automatic solvers. As Charlie Brooker reported back in 2007,

“We’d rendered ourselves obsolete. It was 100% uncensored computer-on-computer action, with two meat puppets pulling the levers, fooling no one but themselves.”

Back to the present, and online Scrabble appears to have lost its sheen (or lustre, patina, or polish). But in a possible near future, I wonder if some similar issues could arise. Continue reading

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