4 ms·

When I was young and (more) idealistic, my department chair pulled me aside after I’d been dogmatic about something and said “everything is political... The soo

When I was young and (more) idealistic, my department chair pulled me aside after I’d been dogmatic about something and said “everything is political... The sooner you realize that, the better off you’ll be.” It took 10 years for that to really sink in, but I knew that every paper has an angle well before that. There’s a position of inevitability about AI around Google, and, to an extent, I agree. Unlike tobacco, alcohol, firearms, cars, fuel, AI is applied mathematics. It’s virtually impossible to restrict functionally, requiring macro behavioral constraints on businesses and governments that are very difficult to enforce if you want to regulate it. Furthermore, ceding the technological lead and removing the business incentive likely has defense (read: offense) implications on the global stage. Some things are going to be hard to explain. Some skin tones, hair styles, accents, pitches, timbres, writing styles, etc. may have more/less separable entropy, and that’s going to inevitably make models seem racist/sexist for some applications. However, we’re pretty far from those edges. Instead, our model biases have likely been driven by our datasets, drawn from our coworkers, family, and friends. In this industry, that’s currently a recipe for over sampling in some demographics and under sampling in others.

_cs2017_ 6y ago
Pick randomly a list of top papers in ML, for example https://www.kdnuggets.com/2020/01/top-10-ai-ml-articles-to-know.html https://www.kdnuggets.com/2020/01/top-10-ai-ml-articles-to-k.... Most (if not all) all of them have no "positive" or "negative" tone, as far as I can tell.
izacus 6y ago
The thing is - I don't think the bias in AI is fixable. The way we fix it is to regulate (not self-regulate, Google, Apple, Facebook and other AI companies aren't trustworthy to do it right) where AI is allowed to be used and how much of a negative impact of a persons livelihood can it impact. In other words, we need to use all the power we have to avoid "The computer said NO, you're homeless/jailed/poor forever now" scenarios.
- benlivengood 6y ago
  Unfortunately a legal solution is weaker because it has to enumerate the precise conditions that are unacceptable. Do you ban the logistic function? Tanh? Statistics? Actuarial tables have been a part of decision-making for a very long time and it's not possible to form a comprehensive legal statement of "all decisions must be made in good faith with respect to inherent biases and to the most rigorous epistemological standards".
  - chaboud 6y ago
    Funny enough, Aggregability is NP-Hard, so comprehensive consideration of the larger set is not actually practical/possible. Here's the proof: http://www.cs.utep.edu/vladik/2006/tr06-29.pdf http://www.cs.utep.edu/vladik/2006/tr06-29.pdf Perhaps there are mechanisms around performance under external test, auditing, or safe harbor due to documented good-faith, but it is not possible to guarantee correctness for the full set from a subset. Similarly, some approaches will plainly fail to perform well with subgroups of people due to sensing technologies. That may appear as model bias, but it's really a matter of physics and available entropy. For the time being, mechanisms that use machine inference as an aid rather than a guide (leaving the judgment to the human in the loop) are probably the best we can hope for.
simion314 6y ago
>Some things are going to be hard to explain. Some skin tones, hair styles, accents, pitches, timbres, writing styles, etc. may have more/less separable entropy, and that’s going to inevitably make models seem racist/sexist for some applications. We all know that if you have garbage as an input the output can be anything. So a professional data scientist should do his job and analyze the data first , identify the problems and if it can't be clean go in the world and collect better data. If you are a high paid professional you should understand that say for face recognition you should get a correct sample size, correct distributed (sorry I am not a professional so i don't know how they should do their job but I know that there are statistical tool that will measure things like this) , instead it seems developers use as input for their experiments some garbage data that they can get their hands on.
- peteradio 6y ago
  If all you are wanting to know is "will this work" it is way to onerous to focus on getting a perfect dataset. If it gets into a product though, that is really pretty bad, but it may be a consequence of their development lifecycle or any other org issue rather than a failure of this or that data scientist.
  - simion314 6y ago
    >If all you are wanting to know is "will this work" it is way to onerous to focus on getting a perfect dataset If the definition of work is relaxed and equivalent to "is better then random and our marketing can sell it" If you are a professional and you data implies that race predicts school grades and you say "it works" instead of "something is not right, let me check the financial status or house size/family size" then you are an impostor, you plug libraries with code from Stack Overflow until it works, doing the right job would give you better results and fix the bias. If you work for Google,Facebook or other giants I am sure there is not an excuse that we don't have the money to hire competent people and cleanup the data or gather correct data. Think about it, because some person/company is incompetent you make life harder for millions of people, why we try to find excuses for not doing the correct thing, we need to demand that this AIs with big consequences are created using mathematically correct methods and when someone reports potential problems we need to stop excusing the guys that want to silence them because bad PR.
kovac 6y ago
> every paper has an angle Not really sure if this is true in general. May be true of commercial entities, but I really hope this isn't true in general. > Furthermore, ceding the technological lead and removing the business incentive likely has defense (read: offense) implications on the global stage. Does it though? Uncovering faults and cracks of a system does exactly the opposite. Discouraging experts from looking for those faults may give the perception of lead, for a while, but is a dangerous mistake to make in the long run. Interfering with experts by pulling them out of, say, academia by paying them more and turning them into your PR tools, I think, is detrimental to the advancement of technology. We may be losing a whole generation of scientists to AdTech when they could have been doing more worthwhile work elsewhere(which, by the way, is why we are here in the first place - someone toiled away at a university to come up with a genius idea to design an artificial perceptron). As to your claim about whether these companies will play a pivotal role in paradigm shifting technology advancements like fundamental research has in the past remains yet to be seen.
- deleted 6y ago
  [deleted]
- chaboud 6y ago
  I don't think that uncovering faults and cracks is a disincentive. Prohibiting use of machine learning techniques and technologies definitely reduces the addressible market, and, thus, profit incentive. There are plenty of reasons to do research and make strides, but "big pot of gold at the end of the rainbow" is one we have to acknowledge the existence of. As far as paper angle goes, sometimes the angle is "what we spent the last year doing is noteworthy enough to warrant publication". Read every paper skeptically. Make it prove to you that it has the data, rigor, and reproducibility to support its claims.
blackbrokkoli 6y ago
This is such a tired argument. Sure, politics is in everything down to the design of the armchair you are arguing from. That however doesn't fucking mean that all papers shall be considered equal? Do you think you would be able to differentiate the value of a census statistics from a 2015 paper written by the state of Nevada from a 1930 census statistic paper from Nazi Germany? Excellent! Because very obviously there is a line where science crosses into bullshit. Sure, the line is multidimensional, ever changing and very blurry. As you say, from finance to family everything factors in and there is no true objective way of doing science. That does not however mean we as humanity can get away with defeationist pseudo-enlightenment by just giving up every analysis of nuance or motive. What Google is doing here is not in the territory of science anymore - you can and should argue that - and should be treated as such. The price of freedom is eternal vigilance, whataboutism won't cut it. Edit: grammar
- joshuaissac 6y ago
  > 1930 census statistic paper from Nazi Germany Minor point, but there was no Nazi Germany in 1930. They gained control of the government in 1932-1933.
- chaboud 6y ago
  I’m saying that papers should be read with a default position of skepticism and consideration of the motivations of the authors (and they all have an angle). When I read an ML paper with authors from Google, I assume they have an angle. When I read an ML Ethics paper with authors from Google, that already went double. Now it’s triple. So look at my argument differently. Every single scientific paper you read should be read as non-objective as a starting position. If it can overcome starting from that position with data, methodology, and reasoning, it’s a solid scientific paper.

_cs2017_ 6y ago

Pick randomly a list of top papers in ML, for example https://www.kdnuggets.com/2020/01/top-10-ai-ml-articles-to-know.html https://www.kdnuggets.com/2020/01/top-10-ai-ml-articles-to-k.... Most (if not all) all of them have no "positive" or "negative" tone, as far as I can tell.

izacus 6y ago

The thing is - I don't think the bias in AI is fixable. The way we fix it is to regulate (not self-regulate, Google, Apple, Facebook and other AI companies aren't trustworthy to do it right) where AI is allowed to be used and how much of a negative impact of a persons livelihood can it impact. In other words, we need to use all the power we have to avoid "The computer said NO, you're homeless/jailed/poor forever now" scenarios.

benlivengood 6y ago
Unfortunately a legal solution is weaker because it has to enumerate the precise conditions that are unacceptable. Do you ban the logistic function? Tanh? Statistics? Actuarial tables have been a part of decision-making for a very long time and it's not possible to form a comprehensive legal statement of "all decisions must be made in good faith with respect to inherent biases and to the most rigorous epistemological standards".
- chaboud 6y ago
  Funny enough, Aggregability is NP-Hard, so comprehensive consideration of the larger set is not actually practical/possible. Here's the proof: http://www.cs.utep.edu/vladik/2006/tr06-29.pdf http://www.cs.utep.edu/vladik/2006/tr06-29.pdf Perhaps there are mechanisms around performance under external test, auditing, or safe harbor due to documented good-faith, but it is not possible to guarantee correctness for the full set from a subset. Similarly, some approaches will plainly fail to perform well with subgroups of people due to sensing technologies. That may appear as model bias, but it's really a matter of physics and available entropy. For the time being, mechanisms that use machine inference as an aid rather than a guide (leaving the judgment to the human in the loop) are probably the best we can hope for.

simion314 6y ago

>Some things are going to be hard to explain. Some skin tones, hair styles, accents, pitches, timbres, writing styles, etc. may have more/less separable entropy, and that’s going to inevitably make models seem racist/sexist for some applications. We all know that if you have garbage as an input the output can be anything. So a professional data scientist should do his job and analyze the data first , identify the problems and if it can't be clean go in the world and collect better data. If you are a high paid professional you should understand that say for face recognition you should get a correct sample size, correct distributed (sorry I am not a professional so i don't know how they should do their job but I know that there are statistical tool that will measure things like this) , instead it seems developers use as input for their experiments some garbage data that they can get their hands on.

peteradio 6y ago
If all you are wanting to know is "will this work" it is way to onerous to focus on getting a perfect dataset. If it gets into a product though, that is really pretty bad, but it may be a consequence of their development lifecycle or any other org issue rather than a failure of this or that data scientist.
- simion314 6y ago
  >If all you are wanting to know is "will this work" it is way to onerous to focus on getting a perfect dataset If the definition of work is relaxed and equivalent to "is better then random and our marketing can sell it" If you are a professional and you data implies that race predicts school grades and you say "it works" instead of "something is not right, let me check the financial status or house size/family size" then you are an impostor, you plug libraries with code from Stack Overflow until it works, doing the right job would give you better results and fix the bias. If you work for Google,Facebook or other giants I am sure there is not an excuse that we don't have the money to hire competent people and cleanup the data or gather correct data. Think about it, because some person/company is incompetent you make life harder for millions of people, why we try to find excuses for not doing the correct thing, we need to demand that this AIs with big consequences are created using mathematically correct methods and when someone reports potential problems we need to stop excusing the guys that want to silence them because bad PR.

kovac 6y ago

> every paper has an angle Not really sure if this is true in general. May be true of commercial entities, but I really hope this isn't true in general. > Furthermore, ceding the technological lead and removing the business incentive likely has defense (read: offense) implications on the global stage. Does it though? Uncovering faults and cracks of a system does exactly the opposite. Discouraging experts from looking for those faults may give the perception of lead, for a while, but is a dangerous mistake to make in the long run. Interfering with experts by pulling them out of, say, academia by paying them more and turning them into your PR tools, I think, is detrimental to the advancement of technology. We may be losing a whole generation of scientists to AdTech when they could have been doing more worthwhile work elsewhere(which, by the way, is why we are here in the first place - someone toiled away at a university to come up with a genius idea to design an artificial perceptron). As to your claim about whether these companies will play a pivotal role in paradigm shifting technology advancements like fundamental research has in the past remains yet to be seen.

deleted 6y ago
[deleted]
chaboud 6y ago
I don't think that uncovering faults and cracks is a disincentive. Prohibiting use of machine learning techniques and technologies definitely reduces the addressible market, and, thus, profit incentive. There are plenty of reasons to do research and make strides, but "big pot of gold at the end of the rainbow" is one we have to acknowledge the existence of. As far as paper angle goes, sometimes the angle is "what we spent the last year doing is noteworthy enough to warrant publication". Read every paper skeptically. Make it prove to you that it has the data, rigor, and reproducibility to support its claims.

blackbrokkoli 6y ago

This is such a tired argument. Sure, politics is in everything down to the design of the armchair you are arguing from. That however doesn't fucking mean that all papers shall be considered equal? Do you think you would be able to differentiate the value of a census statistics from a 2015 paper written by the state of Nevada from a 1930 census statistic paper from Nazi Germany? Excellent! Because very obviously there is a line where science crosses into bullshit. Sure, the line is multidimensional, ever changing and very blurry. As you say, from finance to family everything factors in and there is no true objective way of doing science. That does not however mean we as humanity can get away with defeationist pseudo-enlightenment by just giving up every analysis of nuance or motive. What Google is doing here is not in the territory of science anymore - you can and should argue that - and should be treated as such. The price of freedom is eternal vigilance, whataboutism won't cut it. Edit: grammar

joshuaissac 6y ago
> 1930 census statistic paper from Nazi Germany Minor point, but there was no Nazi Germany in 1930. They gained control of the government in 1932-1933.
chaboud 6y ago
I’m saying that papers should be read with a default position of skepticism and consideration of the motivations of the authors (and they all have an angle). When I read an ML paper with authors from Google, I assume they have an angle. When I read an ML Ethics paper with authors from Google, that already went double. Now it’s triple. So look at my argument differently. Every single scientific paper you read should be read as non-objective as a starting position. If it can overcome starting from that position with data, methodology, and reasoning, it’s a solid scientific paper.