5 ms·

Machine learning does not have less bias than human researchers. It is simply magnified at scale. And that scale is exactly the state of the internet. There is

Machine learning does not have less bias than human researchers. It is simply magnified at scale. And that scale is exactly the state of the internet. There is so much data available to study and understand, that we absolutely need better tools, like machine learning or whatever we want to call it, to help us keep up. Shit's moving faster than our human perception can handle, especially for those who didn't grow up with the internet. Yes the data analyctic tools we have right now are premature— like fast food to our productized minds— but they will improve rapidly, as our taste for quality improves. But sure demonizing the things you don't like is one step on the path to learning what's truly valuable.

coldtea 10y ago
>Machine learning does not have less bias than human researchers. It is simply magnified at scale. Scale differences can and often do lead to qualitative differences. Individual (or aggregate) human researchers are not hooked up in huge services to make inferences and deductions automatically about billions of people. Besides those machine learning tools, beside the huge data sets, are programmed in their general framework by human researchers, and are given weights, constraints, and fine-tuning by them, so they have both kinds of biases. >But sure demonizing the things you don't like is one step on the path to learning what's truly valuable. So, kind of like disparaging via a straw-man a speech that offers detailed argumentation?
- AndrewKemendo 10y ago
  Individual (or aggregate) human researchers are not hooked up in huge services to make inferences and deductions automatically about billions of people Yes they (we) are. It's the same data set. TV, movies, papers, internet videos et al. is all the same biased, labeled data that is being fed (watched, listened to etc...) to machines. You automatically make inferences and deduce things about people based on labeling and training of your brain. You're constantly fine tuning by getting new weights about things through interactions with others and media.
  - coldtea 10y ago
    >Yes they (we) are. I didn't say researchers and/or individual people are not making such judgements about billions of others (e.g. "the Chinese suck/are great"). I said they are not "hooked up in huge services" to make them automatically for billions of individuals -- like an ML algorithm used by Google or Amazon or some government agency etc would do.
    - AndrewKemendo 10y ago
      My point is that it's the same thing. Individuals with outsized power and influence can affect billions of people based on their own judgments and implicitly make those judgments automatically for billions. That's what this question is all about - should machine systems be responsible for the kind of sweeping decisions that humans are making on those populations now? Probably.
Singletoned 10y ago
> Machine learning does not have less bias than human researchers. You are right that machine learning gains bias from the humans that created it, but unless they managed to transfer 100% of their biases to it, it will always have less bias.
- chewxy 10y ago
  We impart our bias on ML algorithms by choosing what data to use to train the AI on. The problem, I think is one of self-selection. Consider two hypothetical social networking websites - Friendface and FaceSpace. Friendface's userbase are mostly white users, while FaceSpace catered mostly to urban, black populations. And it would make sense too - you would only join a social network if your friends are on it. If you're white, chances are the majority of your friends are also white. And vice versa. So Friendface is a lot more active on their ML front. The problem is when Friendface releases their data - because they're more active on the ML front, and ML scientists love to not have to collect their data, what happens is more and more models are trained on the Friendface data and more and more models are being optimized based on Friendface data. Apparent "structural" racism happens. Tumblrinas all pounce on it as if it were the biggest oppressive struggles of their lives. A very cute thing to imagine in this scenario would be to imagine FaceSpace suddenly got good at NLP, and open sources their statistical language model. Recall that FaceSpace users are more likely to use AAVE in their communication, so what do you think the statistical language model would be? In the original article, Maciej mentions "going to the community" - using crowd wisdom to handle these sorts of thing, and preferring to use open standards as opposed to silo'd standards (like writing your blog post on facebook... why??!!). While that sounds like a good idea, like I've mentioned in my other comment, it also sounds tiring as hell. Firms act rationally (more or less)... ML is driven by huge companies with huge datasets. Why would they need to prune external datasets when they could just do their ML research with a few SQL queries?
ohthehugemanate 10y ago
Bias is a pattern-generation process. Machine learning is a pattern-recognition process. Any bias on the part of the (human) data collection, or the (human) training program author, gets spit out as a "pattern", because it is one. The problem is that it gives the illusion of a bias in reality. My go-to example is machine learning police enforcement direction, often used as a counter to racially biased policing. This works in any city with a historical problem of racial bias in policework. We give the algorithm all the data we have from the last 60 years of policing this city. Patrol schedules, incident records, arrest records... everything. The computer magically tells us where we should focus our efforts. To the police chief who paid for the system, and especially to the media reporting on it, it looks like a computer is making the decisions without bias. Hooray! Of course, anyone who's ever worked with machine learning can spot the problem. The data set was generated by racially biased policing. That bias will be reflected in all the records: more arrests for race X, more patrols scheduled through their neighborhoods, more incident reports from those areas. So when the algorithm says "increase patrols in this neighborhood," or "look for people who fit this profile," it is simply synthesizing the patterns from 60 years of racial bias. So the police in LA have a real problem: their "unbiased" computer program is telling them that their criminals look like black people, and they should increase patrols in Compton. So they do, and that data only takes the data further from "un-biased" reality. In fact, the police "black box" is only pointing out a history of racially biased policing. We're relabeling it as recommendations for future behavior.
- yummyfajitas 10y ago
  Of course, in reality this bias can be corrected for. I don't know if specific crime-stat software does it, but it's certainly doable. Here's an example where I solve literally the same problem (better measurement => more events detected) in a different scenario. Is it remotely controversial that I can do this for a sensor network? https://www.chrisstucchio.com/blog/2016/delayed_reactions.html https://www.chrisstucchio.com/blog/2016/delayed_reactions.ht... You might also be interested to know that a variety of studies have shown that policing is not particularly biased. Arrest statistics and the like correspond pretty well with NCVS and similar crime victim surveys. http://slatestarcodex.com/2014/11/25/race-and-justice-much-more-than-you-wanted-to-know/ http://slatestarcodex.com/2014/11/25/race-and-justice-much-m...
yummyfajitas 10y ago
This is simply not true. Most algorithms can and will correct for biases in their inputs. See this (somewhat technical) article where I go into explicit (simulations in numpy) levels of detail: https://www.chrisstucchio.com/blog/2016/alien_intelligences_and_discriminatory_algorithms.html https://www.chrisstucchio.com/blog/2016/alien_intelligences_... The best analogy I've come up with for the non-technical is that algorithms are like humans trying to draw inferences on octopus society. Some octopi might have bias against some other octopi, but it's the height of octopusthromorphism to to expect a human to reproduce that bias.
- wrsh07 10y ago
  This is very optimistic. There are well known and documented cases of ml algorithm bias and its cause [1]. And it's not surprising that data itself contains some biases from the humans creating it. Suppose police are asking machine learning where more crime is committed - there will be a feedback loop. Where are they currently making more arrests? If they spend more time there, the bias will be exaggerated. The op correctly gauges how we should be cautious. Your post, I'm afraid, is misleading at best. [1] https://www.google.com/amp/s/www.technologyreview.com/s/601775/why-we-should-expect-algorithms-to-be-biased/amp/?client=ms-android-google https://www.google.com/amp/s/www.technologyreview.com/s/6017...
  - yummyfajitas 10y ago
    Of course data contains biases. But again, please read the article I linked; algorithms will have a tendency to correct that bias. The examples in the article you link to are not algorithmic bias at all. They consist of: 1) Humans at Facebook manipulating trending results. 2) Google's keyword algorithm (accurately) reflecting the fact that people with black names are more likely to have arrest records. Lets distinguish "bias" from "accurately learning things you wish it wouldn't learn" or "accurately learning things you wish weren't true." None of what I'm saying is remotely controversial. If I told you statistics could detect and correct bias in a mobile phone compass, you'd just think "cool stats bro". Is this article remotely controversial? https://www.chrisstucchio.com/blog/2016/bayesian_calibration_of_mobile_phone_compass.html https://www.chrisstucchio.com/blog/2016/bayesian_calibration... The specific feedback loop you describe - variable detection probability => variable # of detections - can be directly mitigated. For a non-controversial example drawn from sensor networks (sensors report events with a delayed eraction, the longer you wait the more events you detect), see here: https://www.chrisstucchio.com/blog/2016/delayed_reactions.html https://www.chrisstucchio.com/blog/2016/delayed_reactions.ht... (You can find similar examples all over the place. I just link to the ones I wrote because they spring immediately to mind.) In a compass, a sensor network, adtech or other quant finance, the idea that machine learning can fix biased inputs is not remotely controversial. The concept that statistics suddenly stops working to fix racism is just silly anthropomorphism.

coldtea 10y ago

>Machine learning does not have less bias than human researchers. It is simply magnified at scale. Scale differences can and often do lead to qualitative differences. Individual (or aggregate) human researchers are not hooked up in huge services to make inferences and deductions automatically about billions of people. Besides those machine learning tools, beside the huge data sets, are programmed in their general framework by human researchers, and are given weights, constraints, and fine-tuning by them, so they have both kinds of biases. >But sure demonizing the things you don't like is one step on the path to learning what's truly valuable. So, kind of like disparaging via a straw-man a speech that offers detailed argumentation?

AndrewKemendo 10y ago
Individual (or aggregate) human researchers are not hooked up in huge services to make inferences and deductions automatically about billions of people Yes they (we) are. It's the same data set. TV, movies, papers, internet videos et al. is all the same biased, labeled data that is being fed (watched, listened to etc...) to machines. You automatically make inferences and deduce things about people based on labeling and training of your brain. You're constantly fine tuning by getting new weights about things through interactions with others and media.
- coldtea 10y ago
  >Yes they (we) are. I didn't say researchers and/or individual people are not making such judgements about billions of others (e.g. "the Chinese suck/are great"). I said they are not "hooked up in huge services" to make them automatically for billions of individuals -- like an ML algorithm used by Google or Amazon or some government agency etc would do.
  - AndrewKemendo 10y ago
    My point is that it's the same thing. Individuals with outsized power and influence can affect billions of people based on their own judgments and implicitly make those judgments automatically for billions. That's what this question is all about - should machine systems be responsible for the kind of sweeping decisions that humans are making on those populations now? Probably.

Singletoned 10y ago

> Machine learning does not have less bias than human researchers. You are right that machine learning gains bias from the humans that created it, but unless they managed to transfer 100% of their biases to it, it will always have less bias.

chewxy 10y ago
We impart our bias on ML algorithms by choosing what data to use to train the AI on. The problem, I think is one of self-selection. Consider two hypothetical social networking websites - Friendface and FaceSpace. Friendface's userbase are mostly white users, while FaceSpace catered mostly to urban, black populations. And it would make sense too - you would only join a social network if your friends are on it. If you're white, chances are the majority of your friends are also white. And vice versa. So Friendface is a lot more active on their ML front. The problem is when Friendface releases their data - because they're more active on the ML front, and ML scientists love to not have to collect their data, what happens is more and more models are trained on the Friendface data and more and more models are being optimized based on Friendface data. Apparent "structural" racism happens. Tumblrinas all pounce on it as if it were the biggest oppressive struggles of their lives. A very cute thing to imagine in this scenario would be to imagine FaceSpace suddenly got good at NLP, and open sources their statistical language model. Recall that FaceSpace users are more likely to use AAVE in their communication, so what do you think the statistical language model would be? In the original article, Maciej mentions "going to the community" - using crowd wisdom to handle these sorts of thing, and preferring to use open standards as opposed to silo'd standards (like writing your blog post on facebook... why??!!). While that sounds like a good idea, like I've mentioned in my other comment, it also sounds tiring as hell. Firms act rationally (more or less)... ML is driven by huge companies with huge datasets. Why would they need to prune external datasets when they could just do their ML research with a few SQL queries?

ohthehugemanate 10y ago

Bias is a pattern-generation process. Machine learning is a pattern-recognition process. Any bias on the part of the (human) data collection, or the (human) training program author, gets spit out as a "pattern", because it is one. The problem is that it gives the illusion of a bias in reality. My go-to example is machine learning police enforcement direction, often used as a counter to racially biased policing. This works in any city with a historical problem of racial bias in policework. We give the algorithm all the data we have from the last 60 years of policing this city. Patrol schedules, incident records, arrest records... everything. The computer magically tells us where we should focus our efforts. To the police chief who paid for the system, and especially to the media reporting on it, it looks like a computer is making the decisions without bias. Hooray! Of course, anyone who's ever worked with machine learning can spot the problem. The data set was generated by racially biased policing. That bias will be reflected in all the records: more arrests for race X, more patrols scheduled through their neighborhoods, more incident reports from those areas. So when the algorithm says "increase patrols in this neighborhood," or "look for people who fit this profile," it is simply synthesizing the patterns from 60 years of racial bias. So the police in LA have a real problem: their "unbiased" computer program is telling them that their criminals look like black people, and they should increase patrols in Compton. So they do, and that data only takes the data further from "un-biased" reality. In fact, the police "black box" is only pointing out a history of racially biased policing. We're relabeling it as recommendations for future behavior.

yummyfajitas 10y ago
Of course, in reality this bias can be corrected for. I don't know if specific crime-stat software does it, but it's certainly doable. Here's an example where I solve literally the same problem (better measurement => more events detected) in a different scenario. Is it remotely controversial that I can do this for a sensor network? https://www.chrisstucchio.com/blog/2016/delayed_reactions.html https://www.chrisstucchio.com/blog/2016/delayed_reactions.ht... You might also be interested to know that a variety of studies have shown that policing is not particularly biased. Arrest statistics and the like correspond pretty well with NCVS and similar crime victim surveys. http://slatestarcodex.com/2014/11/25/race-and-justice-much-more-than-you-wanted-to-know/ http://slatestarcodex.com/2014/11/25/race-and-justice-much-m...

yummyfajitas 10y ago

This is simply not true. Most algorithms can and will correct for biases in their inputs. See this (somewhat technical) article where I go into explicit (simulations in numpy) levels of detail: https://www.chrisstucchio.com/blog/2016/alien_intelligences_and_discriminatory_algorithms.html https://www.chrisstucchio.com/blog/2016/alien_intelligences_... The best analogy I've come up with for the non-technical is that algorithms are like humans trying to draw inferences on octopus society. Some octopi might have bias against some other octopi, but it's the height of octopusthromorphism to to expect a human to reproduce that bias.

wrsh07 10y ago
This is very optimistic. There are well known and documented cases of ml algorithm bias and its cause [1]. And it's not surprising that data itself contains some biases from the humans creating it. Suppose police are asking machine learning where more crime is committed - there will be a feedback loop. Where are they currently making more arrests? If they spend more time there, the bias will be exaggerated. The op correctly gauges how we should be cautious. Your post, I'm afraid, is misleading at best. [1] https://www.google.com/amp/s/www.technologyreview.com/s/601775/why-we-should-expect-algorithms-to-be-biased/amp/?client=ms-android-google https://www.google.com/amp/s/www.technologyreview.com/s/6017...
- yummyfajitas 10y ago
  Of course data contains biases. But again, please read the article I linked; algorithms will have a tendency to correct that bias. The examples in the article you link to are not algorithmic bias at all. They consist of: 1) Humans at Facebook manipulating trending results. 2) Google's keyword algorithm (accurately) reflecting the fact that people with black names are more likely to have arrest records. Lets distinguish "bias" from "accurately learning things you wish it wouldn't learn" or "accurately learning things you wish weren't true." None of what I'm saying is remotely controversial. If I told you statistics could detect and correct bias in a mobile phone compass, you'd just think "cool stats bro". Is this article remotely controversial? https://www.chrisstucchio.com/blog/2016/bayesian_calibration_of_mobile_phone_compass.html https://www.chrisstucchio.com/blog/2016/bayesian_calibration... The specific feedback loop you describe - variable detection probability => variable # of detections - can be directly mitigated. For a non-controversial example drawn from sensor networks (sensors report events with a delayed eraction, the longer you wait the more events you detect), see here: https://www.chrisstucchio.com/blog/2016/delayed_reactions.html https://www.chrisstucchio.com/blog/2016/delayed_reactions.ht... (You can find similar examples all over the place. I just link to the ones I wrote because they spring immediately to mind.) In a compass, a sensor network, adtech or other quant finance, the idea that machine learning can fix biased inputs is not remotely controversial. The concept that statistics suddenly stops working to fix racism is just silly anthropomorphism.