Posts tagged algorithm
“If we imagine a worm that crawls around the periphery of the forest, seeing a ‘(’ whenever it passes the left edge of a node and a ‘)’ whenever it passes a node’s right edge, that worm will have reconstructed the original string“
“algorithm which is infinite in nature”
For centuries, humans have been creating ever-more complicated systems, from the machines we live with to the informational systems and laws that keep our global civilisation stitched together. Technology continues its fantastic pace of accelerating complexity — offering efficiencies and benefits that previous generations could not have imagined — but with this increasing sophistication and interconnectedness come complicated and messy effects that we can’t always anticipate. It’s one thing to recognise that technology continues to grow more complex, making the task of the experts who build and maintain our systems more complicated still, but it’s quite another to recognise that many of these systems are actually no longer completely understandable. We now live in a world filled with incomprehensible glitches and bugs. When we find a bug in a video game, it’s intriguing, but when we are surprised by the very infrastructure of our society, that should give us pause.
For one British university, what began as a time-saving exercise ended in disgrace when a computer model set up to streamline its admissions process exposed - and then exacerbated - gender and racial discrimination. As detailed here in the British Medical Journal, staff at St George’s Hospital Medical School decided to write an algorithm that would automate the first round of its admissions process. The formulae used historical patterns in the characteristics of candidates whose applications were traditionally rejected to filter out new candidates whose profiles matched those of the least successful applicants. By 1979 the list of candidates selected by the algorithms was a 90-95% match for those chosen by the selection panel, and in 1982 it was decided that the whole initial stage of the admissions process would be handled by the model. Candidates were assigned a score without their applications having passed a single human pair of eyes, and this score was used to determine whether or not they would be interviewed. Quite aside from the obvious concerns that a student would have upon finding out a computer was rejecting their application, a more disturbing discovery was made. The admissions data that was used to define the model’s outputs showed bias against females and people with non-European-looking names.