How a Machine Learns Prejudice – Scientific American
Agreed, this is a big issue.
If artificial intelligence takes over our lives, it probably won’t involve humans battling an army of robots that relentlessly apply Spock-like logic as they physically enslave us. Instead, the machine-learning algorithms that already let AI programs recommend a movie you’d like or recognize your friend’s face in a photo will likely be the same ones that one day deny you a loan, lead the police to your neighborhood or tell your doctor you need to go on a diet. And since humans create these algorithms, they’re just as prone to biases that could lead to bad decisions—and worse outcomes. These biases create some immediate concerns about our increasing reliance on artificially intelligent technology, as any AI system designed by humans to be absolutely “neutral” could still reinforce humans’ prejudicial thinking instead of seeing through it.
(tags: prejudice bias machine-learning ml data training race racism google facebook)
Falsehoods Programmers Believe About CSVs
Much of my professional work for the last 10+ years has revolved around handing, importing and exporting CSV files. CSV files are frustratingly misunderstood, abused, and most of all underspecified. While RFC4180 exists, it is far from definitive and goes largely ignored. Partially as a companion piece to my recent post about how CSV is an encoding nightmare, and partially an expression of frustration, I’ve decided to make a list of falsehoods programmers believe about CSVs. I recommend my previous post for a more in-depth coverage on the pains of CSVs encodings and how the default tooling (Excel) will ruin your day.
(via Tony Finch)(tags: via:fanf csv excel programming coding apis data encoding transfer falsehoods fail rfc4180)