Originally published at: https://boingboing.net/2018/05/29/gigo-gigo-gigo.html
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Bathed in his currents of liquid helium, self-contained, immobile, vastly well informed by every mechanical sense: Shalmaneser.
Every now and again there passes through his circuits a pulse which carries the cybernetic equivalent of the phrase, “Christ, what an imagination I’ve got.“
John Brunner, Stand on Zanzibar.
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This reminds me of the US Military project to train drones to spot tanks. They used a helicopter to take a lot of photos of tanks one day, and then shot the same locations the next day without the tanks. Working off the photos, the control system could spot a photo containing a tank with 100% accuracy. Then someone thought to try the system with some different photos. It turned out that they’d spent a pretty penny training a computer to spot the difference between a sunny day (day 1) and an overcast day (day 2).
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I was just listening to a BBC program where a machine learning researcher was reporting that she has had to wear a blank white mask for a (borrowed) algorithem to recognize her face as she has very dark skin.
I am ‘verryy’ reassured with the deployment of new facial recognition software.
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In the words of Hulk Hogan, “Amen, brother!” The training/test sets are SAMPLES! How to assess whether they are “good”/“bad” remains a sticky-wicket.
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This might just be a blacklist of categories you never want to predict, because the cost of a false positive is so high
Like, for example:
If you want good examples of things you shouldn’t use machine learning to predict, look at the things we are using it to predict.
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Except, of course, rich abusers…
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A longer treatment of this issue at the Canadian magazine, The Walrus.
To get you interested, here’s an excerpt:
“More than 99 percent of the time, the systems correctly identified a lighter-skinned man. But that’s no great feat when data sets skew heavily toward white men; in another widely used data set, the training photos used to make identifications are of a group that’s 78 percent male and 84 percent white. When Buolamwini tested the facial-recognition programs on photographs of black women, the algorithm made mistakes nearly 34 percent of the time. And the darker the skin, the worse the programs performed, with error rates hovering around 47 percent— the equivalent of a coin toss. The systems didn’t know a black woman when they saw one.”
…so hire an all-black, all-female crew for your next heist!!
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That’s not ML. You can do that with a few lines of Perl.
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I took this as just an example. I’ve cleaned up a lot of data. Often once you are aware of a problem you can solve it with a careful find and replace. It’s becoming aware of the problems that takes work.
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THIS TIMES INFINITY! (and beyond!)
If your fancy ML system is giving you garbage results, look at what you are feeding it- perhaps it is garbage.
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Was that researcher Joy Buolamwini?
I didn’t find a BBC audio program, but I did find these:
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This is why you need to go into ML with a problem/question in mind.
All too often I see people jumping into ML by feeding it data but without any question in mind, which then leads to frustration because the model isn’t giving them useful data.
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42!
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Hah, great example! That is the main problem with machine learning algorithms: unlike most statistical models, which allow you to more or less understand how they classify something, a machine learning algorithm is a black box. Enter LIME to the rescue! This is an approach that opens the black box and visualizes an algorithm’s inner workings…
In the paper, the authors use a similar neat example (Section 6.4): to train a classifier to distinguish between huskies and wolves, they purposely fed it pictures of huskies and wolves where all the wolf pictures have snow in the background. Result: the classifier classifies anything as a wolf as long as it has snow in the background.
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The wisdom of the ages:
- Sanitize your inputs
- Always keep your optics clean.
- Never get involved in a land war in Asia.
- Do not taunt Happy Funball™.
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I first heard this story in the early 1990’s, so the problem goes way back.
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For me, the 1990s are still recent 
Waddaya mean, that’s getting on for thirty years ago?! I demand a re-count!
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That’s also the part that really takes real intelligence.
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The two iron laws of computing:
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GIGO.
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Every new generation of programmers has some PFYs in it that think they found a way to magically circumvent GIGO.
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