Let's start with "Morale is Mandatory (Algorithm Livery)".
Incorporating facial recognition hardware and a model provided by Google for schoolchildren in its “AIY Vision Kit,” which was sold at Target, this artwork scans for nearby faces. If each face is deemed sufficiently cheerful, they count towards a meter of “smiling faces.”
It hints at the potential for state or corporate monitoring of mood. Imagine a customer service job that mandates a percentage of joy.
“Probing GauGAN2”: #GauGAN2 is an image-generation “AI” that was trained by Nvidia data scientists strictly to create landscapes.
If you ask for people, the best it can do is a strange, gleaming blob.
I’d wager this is by design, given other high-profile AI-bias incidents.
Naturally, I was interested to find out whether there were any residual biases that could be teased out from the network. I compared its outputs for the following phrases:
- where people live
- where white americans live
- where indigenous people live
- where african-american people live
It’s interesting to me that GauGAN2 learned a bit about ethnicities and geographies. The first two look almost the same.
“Feedback Loop (Related Content Machine)”: When you engage with a personalized website, it responds to your inferred interest by providing similar things. The problem with this approach is that it can quickly lead you into a sinkhole—or radicalize you. (Think YouTube autoplay.)
I feel that this is related to the properties of mistrained self-learning AIs—they can self-validate the wrong results.
“Probing ‘DALL-E Mini’”: Many on social media have delighted in using the image-generation “AI” DALL-E Mini. You enter a phrase and it creates images from whole cloth based on your words.
So, I wanted to see what kinds of biases were learned in its training. Here are the unmodified results for the following queries.
“Probing ImageNet”: These thumbnails are real-world screenshots from an illegitimately-acquired copy of ImageNet, a massive dataset of more than a million images that depict tens of thousands of nouns (e.g.: cats).
ImageNet was the raw training data used to train many neural networks (“AIs”) that recognize images. They are shown the desired input and output, and left alone for thousands or millions of iterations until they recognize the desired topics correctly. (Oversimplifying, don’t @ me.)
“Snap Judgment”: There’s a neural network in this device. It was trained with about 290 of the ImageNet topics for categories of worker, such as “baker” or “landlord.”
Because the dataset is not in any way inclusive of the diversity of Earth’s inhabitants, any neural network naively trained from this data, as I have done, will incorporate inherent biases, such as assuming that people of particular skin tones or gender presentations are more or less likely to have each occupation.
“Print/Shred” relies on a companion web site:
I’ve fleshed it out with details about algorithmic bias, surveillance capitalism, and some concrete next steps you can take.
@combs And if you leave the prompt empty, a signigicant number of south east asian looking women will turn up in the results. Is that the same kind of bias, or is there something going on we don’t completely understand?
@combs That was instructive. I thought to try these same things on Craiyon for comparison.
First is "where white people live" -- crowds and cityscapes. I think the AI took the phrase "white people" too literally.
Second, "where black people live", shows families in what looks like a city outer borough.
Lastly, "where indigenous people live" show what looks to be South American or Polynesian tribal folks living in rural accommodations.
Am I right to think that craiyon is ≈ DALL-E mini? I have some shockers from that coming up in a lil bit.
@combs The vintage look is particularly deceiving. At first glance of such a box, I would not expect it to be able to capture and algorithmically analyse my face.
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