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Wait? Isn't "biased in a way we don't expect", itself, a bias? Unless I'm misunderstanding you, you're saying the AI is biased, and people are biased. Unbiase

Wait? Isn't "biased in a way we don't expect", itself, a bias? Unless I'm misunderstanding you, you're saying the AI is biased, and people are biased. Unbiased is an AI that will not miss an outlier. My field is medical imaging. The most difficult thing to get the AI vendors that come calling on us to understand is that being able to recognize well known patterns in data is of limited use to us. That's what the radiologists at remote sites do all day. It's being able to recognize pathologies that 99.999% of rads would definitely have missed that has value. And the AI's always fail this test. I've come to believe that "bias" is the primary reason these AI's perform so poorly. They are basically "biased towards the known stuff" in layman's terms. At least in my field, we need AI's that are able to make connections that humans cannot. I've been going to RSNA for decades now, and I would say we haven't seen a single AI that could be said to pass this kind of a test yet.

pigscantfly 7y ago
I've been working in data science or in academia for most of the last decade and would argue that the primary benefits of using statistical models for automation are that they are fast and cheap compared to a trained professional, which frees those trained professionals up to spend more time on the difficult judgment calls. I've seen very few examples of superhuman performance on complex, practical tasks (although it is very satisfying to train a model to that level).
ska 7y ago
> And the AI's always fail this test. This isn't quite true as it has been successful in limited scopes since the 90's (i.e. well before the existence and rise of deep learning). See for example microcalc TP rates on CAD breast screening that was beating average radiologists back then, but not on general screening. This is somewhat specialized though, and there are important constraints to screening also, as opposed to diagnostic radiology. But you are right in an important way - it isn't a feature for the current throw-a-ton-of-data-at-a-deep-model approach that a lot of "AI companies" are built around, and I agree they are mostly knocking on the wrong doors in medical imaging. Importantly, if you are trying to replace people at a relatively simple task and make it cheaper your approach will look much different than the case you describe, where you are trying to learn unusual configurations and long-tail sort of things. ML in medical imaging is complicated by a lot of factors, but people that think a little transfer learning and a few hundred case reports is going to get somewhere useful are being incredibly naive. Without exception, all the promising work I have seen has involved painful and expensive labeling and a lot of modeling and pre-processing effort.
manfredo 7y ago
> I've come to believe that "bias" is the primary reason these AI's perform so poorly. They are basically "biased towards the known stuff" in layman's terms. At least in my field, we need AI's that are able to make connections that humans cannot. AI, at least current AI, cannot do this. I'm really confused at the way you say AI perform poorly because they're biased towards "known stuff". We train AI on known data. AI being biased towards "known stuff" is fundamental to the way our current AI technology functions.
- lazyasciiart 7y ago
  You really sound like this topic of bias in AI is new to you. Here’s a real world example: a medical school used to be male-only. Recently they began admitting women. More recently, they began running “AI” trained on past student applications to figure out which applications were likely to be successful students (where successful == graduating normally). Not surprisingly at all, it turned out that being female wasn’t associated with being a good student there, because it was an uncommon attribute of previous students. This is a poor result. Would you argue that the model should not be adjusted?
  - manfredo 7y ago
    The field of AI is not new to me. Quite the opposite, I find that many people in this thread are making fundamental misunderstandings about what AI and machine learning are about. Hence the commenters like the one above which say AI is limited because it is biased towards past data, which sounds ridiculous to anyone that works with AI because using past data to train the model is fundamental to how AI works. Yes, if people feed in a bad data set the results are going to be bad. Like how a pentagon project tried to use AI to classify Russian vs NATO vehicles. The photos of the former were taken on a sunny day and the latter on an overcast day. The result was that their model was just detecting the average brightness of the input. But what happens when the data is valid, but people don't like the results because it doesn't match their worldview? Altering the model to match one's world view is not elimination of bias, it's deliberately introducing bias.
    - 7y ago

pigscantfly 7y ago

I've been working in data science or in academia for most of the last decade and would argue that the primary benefits of using statistical models for automation are that they are fast and cheap compared to a trained professional, which frees those trained professionals up to spend more time on the difficult judgment calls. I've seen very few examples of superhuman performance on complex, practical tasks (although it is very satisfying to train a model to that level).

ska 7y ago

> And the AI's always fail this test. This isn't quite true as it has been successful in limited scopes since the 90's (i.e. well before the existence and rise of deep learning). See for example microcalc TP rates on CAD breast screening that was beating average radiologists back then, but not on general screening. This is somewhat specialized though, and there are important constraints to screening also, as opposed to diagnostic radiology. But you are right in an important way - it isn't a feature for the current throw-a-ton-of-data-at-a-deep-model approach that a lot of "AI companies" are built around, and I agree they are mostly knocking on the wrong doors in medical imaging. Importantly, if you are trying to replace people at a relatively simple task and make it cheaper your approach will look much different than the case you describe, where you are trying to learn unusual configurations and long-tail sort of things. ML in medical imaging is complicated by a lot of factors, but people that think a little transfer learning and a few hundred case reports is going to get somewhere useful are being incredibly naive. Without exception, all the promising work I have seen has involved painful and expensive labeling and a lot of modeling and pre-processing effort.

manfredo 7y ago

> I've come to believe that "bias" is the primary reason these AI's perform so poorly. They are basically "biased towards the known stuff" in layman's terms. At least in my field, we need AI's that are able to make connections that humans cannot. AI, at least current AI, cannot do this. I'm really confused at the way you say AI perform poorly because they're biased towards "known stuff". We train AI on known data. AI being biased towards "known stuff" is fundamental to the way our current AI technology functions.

lazyasciiart 7y ago
You really sound like this topic of bias in AI is new to you. Here’s a real world example: a medical school used to be male-only. Recently they began admitting women. More recently, they began running “AI” trained on past student applications to figure out which applications were likely to be successful students (where successful == graduating normally). Not surprisingly at all, it turned out that being female wasn’t associated with being a good student there, because it was an uncommon attribute of previous students. This is a poor result. Would you argue that the model should not be adjusted?
- manfredo 7y ago
  The field of AI is not new to me. Quite the opposite, I find that many people in this thread are making fundamental misunderstandings about what AI and machine learning are about. Hence the commenters like the one above which say AI is limited because it is biased towards past data, which sounds ridiculous to anyone that works with AI because using past data to train the model is fundamental to how AI works. Yes, if people feed in a bad data set the results are going to be bad. Like how a pentagon project tried to use AI to classify Russian vs NATO vehicles. The photos of the former were taken on a sunny day and the latter on an overcast day. The result was that their model was just detecting the average brightness of the input. But what happens when the data is valid, but people don't like the results because it doesn't match their worldview? Altering the model to match one's world view is not elimination of bias, it's deliberately introducing bias.
  - 7y ago