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Notes on AI Bias

ben-evans.com

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Re: Notes on AI Bias

#81

Post by bumby »

fvdessen wrote: post #2

> Since Amazon’s current employee base skews male, the examples of ‘successful hires’ also, mechanistically, skewed male and so, therefore, did this system’s selection of resumés. Amazon spotted this and the system was never put into production. Couldn't they have retrained the system with a 50/50 mix of males / females resumes ? Or restrict the use of the algorithm to sort male resumes ? Or maybe resumes don't actua…

Or couldn't they provide data augmentation on the same samples to give the effect of a more diverse (and more populous) training set?

Using the blog's skin cancer example, couldn't the labelled images be augmented by altering the skin tones and adding these new examples to the training set?

It seems to me that some of the anomalous results discussed in the article are actually the result of poor model design or poor pre-processing data choices. We can't just throw anything to any ol' machine learning model and expect it to be magic

Re: Notes on AI Bias

#82

Post by FakeComments »

michaelmior wrote: post #72
FakeComments wrote: post #66

Earlier quoted context omitted.

I agree — race and culture are very different things. That’s why it’s only meaningful to draw these conclusions once you control for cultural variation. No one has shown that they drew conclusions based on race: they drew conclusions based on written words, which correlated with race. The claim is that this demonstrates racial discrimination. However, an equally valid explanation is culture as a confounding variable…

You're right, I didn't try to address it. I also didn't make any assumptions of racism in my comment. However, generally speaking, I don't think one culture is better than another either and discrimination based on culture is also a problem. (I understand that this is an opinion and many would disagree with me on that point.)

It’s an absolute nonsense opinion, because there are obviously cultural beliefs that have an impact on work performance, particularly over a lifetime — eg, beliefs in honesty, hard work, and obtaining education. Different cultures place different values on these things, and it leads to different outcomes — even just looking at the impact of cultural drift over time, among a single people.

I also find it absolutely abhorrent to claim there is no ethical difference in whether or not groups of humans cut the clitorises off young girls or own slaves, as you’ve done here. That’s what it means to say you don’t believe some cultures are better: that it’s just fine to mutilate children and own other humans, because hey, that’s just a cultural belief.

You’re so woke, your brain broke.

Re: Notes on AI Bias

#83

Post by mv4 »

twa927 wrote: post #14

> Until about 2013, If you wanted to make a software system that could, say, recognise a cat in a photo, you would write logical steps. You’d make something that looked for edges in an image, and an eye detector, and a texture analyser for fur, and try to count legs, and so on, and you’d bolt them all together... I'm doing a lot of such algorithms (well, not for images). Does someone know if such algorithms have a na…

this is similar to bag-of-words models

https://en.wikipedia.org/wiki/Bag-of-words_model_in_computer...

Re: Notes on AI Bias

#84

Post by microtherion »

twa927 wrote: post #14

> Until about 2013, If you wanted to make a software system that could, say, recognise a cat in a photo, you would write logical steps. You’d make something that looked for edges in an image, and an eye detector, and a texture analyser for fur, and try to count legs, and so on, and you’d bolt them all together... I'm doing a lot of such algorithms (well, not for images). Does someone know if such algorithms have a na…

I kind of like the tongue in cheek moniker "GOFAI" (Good, Old-Fashioned AI), though that is applied more to symbolic AI https://www.cs.swarthmore.edu/~eroberts/cs91/projects/ethics...

Re: Notes on AI Bias

#85

Post by bumby »

gambler wrote: post #51

>The most obvious and immediately concerning place that this issue can be manifested is in human diversity. I swear, when someone starts building autonomous killer robots, the first set of concerned articles will probably be asking whether robots were properly trained to target all genders and races with equal accuracy. This is not a sensible way to approach AI ethics. >It was recently reported that Amazon had tried…

Isn't this the one of the major concerns of ML, the bias-variance trade-off? By creating a low-variance model, we create a highly biased model that misses some of the important feature relationships necessary to create a truly generalized model?

Meaning, isn't it prudent to spending time on this issue?

Re: Notes on AI Bias

#86

Post by TheCoelacanth »

DuskStar wrote: post #28
TheCoelacanth wrote: post #22

Earlier quoted context omitted.

If the training data is all gas turbines that you own, why do you care about having the ML model at all? You already have complete knowledge of the state of all your gas turbines. There's no point to having an ML model unless you are applying it to something outside of the training data. If you plan on applying the model to different turbines, then there is potential for sample bias in which turbines you selected. If…

Well, I might care about predicting the next turbine to fail. If Siemens sensors are truly unrelated to the issues, that'll average out eventually - but I'd be highly skeptical of someone asserting that it's completely unrelated to the failures and not just covarying with something we're not using as a model input. Why would I care about the fact that only 10% of turbines globally have Siemens sensors? I don't know t…

Next turbine to fail means you sample based on time points, so you still could have sample bias.

Say that turbines have an average lifespan of X years, and from year 0 to 10 you bought 90% Siemens and then from year 10 to 20 you bought 10% Siemens and then you measure failure rates from year X to year X+10.

Based on that data you would predict that Siemens turbines will be the most likely to fail next, but they are probably actually less likely to fail because most of the ones that are likely to fail soon are already gone.

Re: Notes on AI Bias

#87

Post by BickNowstrom »

bumby wrote: post #85
gambler wrote: post #51

>The most obvious and immediately concerning place that this issue can be manifested is in human diversity. I swear, when someone starts building autonomous killer robots, the first set of concerned articles will probably be asking whether robots were properly trained to target all genders and races with equal accuracy. This is not a sensible way to approach AI ethics. >It was recently reported that Amazon had tried…

Isn't this the one of the major concerns of ML, the bias-variance trade-off? By creating a low-variance model, we create a highly biased model that misses some of the important feature relationships necessary to create a truly generalized model? Meaning, isn't it prudent to spending time on this issue?

You are conflating bias (error) with bias (fairness).

Re: Notes on AI Bias

#88

Post by bumby »

gizmo686 wrote: post #52
MiroF wrote: post #41

Earlier quoted context omitted.

> I think you assume here that the historical effects that led to Siemens sensors correlating with failure will continue to be true in the future. Yes, AI systems presume induction to be true. But so does... uh, science and most other things we do?

Science has trained experts thinking about the data. If you set a team of scientists to find a way of predicting failure of turbines, they might notice a correlation between Siemens sensors and failure. They would then look for and attempt to prove theories to explain this descrepency. In doing so, they would likly discover that, not only can they not find a causative theory, but the correlation goes away when they c…

That's an interesting way to frame it. AI may stop at proximate causes rather than finding root causes

Re: Notes on AI Bias

#89

Post by sfreporter »

gambler wrote: post #78
lonnyk wrote: post #70

Earlier quoted context omitted.

I get the point, but why didn't you just exclude intern resumes from the training data? Do you still suspect a skewed result?

>I get the point, but why didn't you just exclude intern resumes from the training data? That was the logical next step and we started on that, but it required exporting more historic data out of the HR system and filtering out anyone who started as an intern as well. Sounds simple, but in practice it's anything but. Just for the reference, data extraction, cleaning and filtering in that project took at least an orde…

@gambler: thank you for reading my reporting. I would love to chat confidentially to understand your perspective better. Please see my HN profile.

Re: Notes on AI Bias

#90

Post by bhl »

twa927 wrote: post #14

> Until about 2013, If you wanted to make a software system that could, say, recognise a cat in a photo, you would write logical steps. You’d make something that looked for edges in an image, and an eye detector, and a texture analyser for fur, and try to count legs, and so on, and you’d bolt them all together... I'm doing a lot of such algorithms (well, not for images). Does someone know if such algorithms have a na…

Heuristic algorithms using hand-crafted features.
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