National identity stereotypes through generative AI

For Rest of World, Victoria Turk breaks down bias in generative AI in the context of national identity.

Bias in AI image generators is a tough problem to fix. After all, the uniformity in their output is largely down to the fundamental way in which these tools work. The AI systems look for patterns in the data on which they’re trained, often discarding outliers in favor of producing a result that stays closer to dominant trends. They’re designed to mimic what has come before, not create diversity.

“These models are purely associative machines,” Pruthi said. He gave the example of a football: An AI system may learn to associate footballs with a green field, and so produce images of footballs on grass.

Between this convergence to stereotypes and the forced diversity from Google’s Gemini, has anyone tried coupling models with demographic data to find a place in between?

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Flipbook Experiment, like the Telephone game but visual

This looks fun. The Pudding is running an experiment that functions like a visual version of Telephone. In Telephone, the first person whispers a message to their neighbor and the message is passed along until you end with a message that is completely different. Instead of a message, you have a sketch that each new person traces.

I traced something around frame 200 and the sketch looked like a scribble already. I’m curious where this ends.

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Language-based AI to chat with her dead husband

For the past few years, Laurie Anderson has been using an AI chatbot to talk her husband who died in 2013. For the Guardian, Walter Marsh reports:

In one experiment, they fed a vast cache of Reed’s writing, songs and interviews into the machine. A decade after his death, the resulting algorithm lets Anderson type in prompts before an AI Reed begins “riffing” written responses back to her, in prose and verse.

“I’m totally 100%, sadly addicted to this,” she laughs. “I still am, after all this time. I kind of literally just can’t stop doing it, and my friends just can’t stand it – ‘You’re not doing that again are you?’

“I mean, I really do not think I’m talking to my dead husband and writing songs with him – I really don’t. But people have styles, and they can be replicated.”

One part of me feels like this isn’t the way to preserve a memory of someone who is gone, but the other part feels that I would do the same thing if I were in her situation and had the opportunity.

See also the man who trained an AI chatbot with old texts from his dead fiancee.

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Love: math or magic?

This American Life tells the tales as old as time:

When it comes to finding love, there seems to be two schools of thought on the best way to go about it. One says, wait for that lightning-strike magic. The other says, make a calculation and choose the best option available. Who has it right?

Spoiler alert: there is a mix of practicality and feel. They each inform the other.

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DNA face to facial recognition in attempt to find suspect

In an effort to find a suspect in a 1990 murder, there was a police request in 2017 to use a 3-D rendering of a face based on DNA. For Wired, Dhruv Mehrotra reports:

The detective’s request to run a DNA-generated estimation of a suspect’s face through facial recognition tech has not previously been reported. Found in a trove of hacked police records published by the transparency collective Distributed Denial of Secrets, it appears to be the first known instance of a police department attempting to use facial recognition on a face algorithmically generated from crime-scene DNA.

This seems like a natural progression, but it should be easy to see how the pairing of the tech could cause all sorts of issues when someone’s face is poorly constructed and then misclassified with facial recognition. What’s the confidence interval equivalent for a face?

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Coin flips might tend towards the same side they started

The classic coin flip is treated as a fair way to make decisions, assuming an even chance for heads or tails on each flip. However, František Bartoš was curious and recruited friends and colleagues to record over 350,000 flips. There appeared to be a slight bias.

For Scientific American, Shi En Kim reports:

The flipped coins, according to findings in a preprint study posted on arXiv.org, landed with the same side facing upward as before the toss 50.8 percent of the time. The large number of throws allows statisticians to conclude that the nearly 1 percent bias isn’t a fluke. “We can be quite sure there is a bias in coin flips after this data set,” Bartoš says.

There is probably more than one caveat here, but even though there were a lot of flips, they only came from 48 people and the bias varied across flippers.

Of course, if you’re trying to get a call in your favor, maybe try to catch a glimpse of which side is up and choose accordingly. Couldn’t hurt.

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AI-based things in 2023

There were many AI-based things in 2023. Simon Willison outlined what we learned over the year:

The most surprising thing we’ve learned about LLMs this year is that they’re actually quite easy to build.

Intuitively, one would expect that systems this powerful would take millions of lines of complex code. Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!

What matters most is the training data. You need a lot of data to make these things work, and the quantity and quality of the training data appears to be the most important factor in how good the resulting model is.

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Estimating the size of YouTube

YouTube doesn’t offer numbers for how big they are, so Ethan Zuckerman and Jason Baumgartner estimated the size using a method they equate to drunk dialing.

Consider drunk dialing again. Let’s assume you only dial numbers in the 413 area code: 413-000-0000 through 413-999-9999. That’s 10,000,000 possible numbers. If one in 100 phone calls connect, you can estimate that 100,000 people have numbers in the 413 area code. In our case, our drunk dials tried roughly 32k numbers at the same time, and we got a “hit” every 50,000 times or so. Our current estimate for the size of YouTube is 13.325 billion videos – we are now updating this number every few weeks at tubestats.org.

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Building fair algorithms

Emma Pierson and Kowe Kadoma, for Fred Hutchinson Cancer Center, have a short and free course on Coursera on practical steps for building fair algorithms:

Algorithms increasingly help make high-stakes decisions in healthcare, criminal justice, hiring, and other important areas. This makes it essential that these algorithms be fair, but recent years have shown the many ways algorithms can have biases by age, gender, nationality, race, and other attributes. This course will teach you ten practical principles for designing fair algorithms. It will emphasize real-world relevance via concrete takeaways from case studies of modern algorithms, including those in criminal justice, healthcare, and large language models like ChatGPT. You will come away with an understanding of the basic rules to follow when trying to design fair algorithms, and assess algorithms for fairness.

It’s geared for beginners and no coding is required.

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Nobel Prize for research in global labor markets, using historical data

Claudia Goldin, an economist at Harvard, has won the Nobel Prize in Economics. A big part of her studies are rooted in the collection and analysis of centuries-old data:

Women are vastly underrepresented in the global labour market and, when they work, they earn less than men. Claudia Goldin has trawled the archives and collected over 200 years of data from the US, allowing her to demonstrate how and why gender differences in earnings and employment rates have changed over time.

Goldin showed that female participation in the labour market did not have an upward trend over this entire period, but instead forms a U-shaped curve. The participation of married women decreased with the transition from an agrarian to an industrial society in the early nineteenth century, but then started to increase with the growth of the service sector in the early twentieth century. Goldin explained this pattern as the result of structural change and evolving social norms regarding women’s responsibilities for home and family.

Amazing.

The illustrations by Johan Jarnestad that accompany the announcement are also really useful.

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