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This is an interesting take, but I think part of what makes us human is our inability to disassociate our “domain knowledge” from our sense of self. Much domai

This is an interesting take, but I think part of what makes us human is our inability to disassociate our “domain knowledge” from our sense of self. Much domain knowledge is in some sense innate or even subconscious, picked up in the field. I think it would be more efficient just to spy on people’s thoughts (and work) in some manner if there is a fear they could misapply their knowledge or leave a critical process hanging.

thriftwy 9y ago
Yes they will allow for a degree of inefficiency if it removes human factor. The reasoning will go, yes we are losing $1 on every automated transaction, but we can make more of them, and we certainly avert one $10,000 corruption case and one $100,000 case where we get sued for human decision. You can't sue machine learning algorighm (that how it would go). But human, you can, even frivolously.
- stefie10 9y ago
  Yes but you can sue the company that owns the machine learning algorithm, probably for much more money than you can sue an individual. You can even imagine class action law suites and the like. (I am one of the researchers mentioned in the article.)
  - thriftwy 9y ago
    You can also have paper trail documenting that you never did anything illicit during the development of the model. There will be protocols for sensitive areas. Can't do the same with individual decision making.
  - fenwick67 9y ago
    It is much harder to ask an algorithm about it's reasoning than a person's. "Joe, why did you fire Carla?" "She was late to work every other day and spent all day on her phone" "HR-bot 5000, why did you fire Carla?" "Weights indices 59738, 837, and 28836 added to over 0.67, yielding a score of only .85, which is below the firing threshold" Certainly you could look at the input data and try to convince a jury that her performance wasn't acceptable but you can't prove that is why the AI fired her.
    - stefie10 9y ago
      We cannot do this today, but this capability, of using language, is one we are actively working towards. Eventually, you can imagine an AI that can engage in a dialog like this one, in words. For example, see the DARPA Explainable AI program: https://www.darpa.mil/program/explainable-artificial-intelligence https://www.darpa.mil/program/explainable-artificial-intelli....
      - fenwick67 9y ago
        Great link, thanks. These are the kind of systems that will really allow AI to take off.
    - fishcolorbrick 9y ago
      At my previous shop, it was normal to silently tolerate developers breaking the rules (not locking their stations when they stepped away, browsing unapproved sites, coming in late, leaving early)... until it was decided that they needed to be fired because they were rude to someone important, or etc... at that point they lost their privilege to act like a professional and their badge-access records were pulled and compared to their time-entry. The reason developers got fired is because of clear and well-defined rules... that were never enforced except when the decision to fire them for an arbitrary reason had already been made. I think HR-bot 5000 would treat employees more equitably by forcing employers to confront the inequity of some HR regulations. The same way self-driving cars "drive weird" because they stop at stop signs and drive the speed limit.
- occultist_throw 9y ago
  Well, where "Machine Learning" (aka: black box you can only get weights for a given question), one can train a machine to be racist, sexist, ageist, or whatever. The problem is that the end weight distribution is different than the GB's or TB's of training data. How do we know the training was fair and impartial? How did the trainers even know if it was? What biases crept in on this stage? Worse yet, what if the bias of the black box does denigrate black people... Say, we take in all pictures of convicted criminals- it's disproportionaly black. I would argue part of that is because of inherent policing biases, but that's embedded in "guilty" verdict. Who's at fault for this "bias"? Is there a fault? How do we detect, other than exhaustively? I'm eagerly awaiting for methods to "open up" ML black boxes and see what makes them tick. See their decision trees, their neural weights. I want to poke and prod to see what's behind those series of numbers like [.888271829 1.10999292992 37.999999921 1000.32 .73] . Right now, it's shove data in exhaustively and hope for the best. I don't particularly care for that way of analysis.

thriftwy 9y ago

Yes they will allow for a degree of inefficiency if it removes human factor. The reasoning will go, yes we are losing $1 on every automated transaction, but we can make more of them, and we certainly avert one $10,000 corruption case and one $100,000 case where we get sued for human decision. You can't sue machine learning algorighm (that how it would go). But human, you can, even frivolously.

stefie10 9y ago
Yes but you can sue the company that owns the machine learning algorithm, probably for much more money than you can sue an individual. You can even imagine class action law suites and the like. (I am one of the researchers mentioned in the article.)
- thriftwy 9y ago
  You can also have paper trail documenting that you never did anything illicit during the development of the model. There will be protocols for sensitive areas. Can't do the same with individual decision making.
- fenwick67 9y ago
  It is much harder to ask an algorithm about it's reasoning than a person's. "Joe, why did you fire Carla?" "She was late to work every other day and spent all day on her phone" "HR-bot 5000, why did you fire Carla?" "Weights indices 59738, 837, and 28836 added to over 0.67, yielding a score of only .85, which is below the firing threshold" Certainly you could look at the input data and try to convince a jury that her performance wasn't acceptable but you can't prove that is why the AI fired her.
  - stefie10 9y ago
    We cannot do this today, but this capability, of using language, is one we are actively working towards. Eventually, you can imagine an AI that can engage in a dialog like this one, in words. For example, see the DARPA Explainable AI program: https://www.darpa.mil/program/explainable-artificial-intelligence https://www.darpa.mil/program/explainable-artificial-intelli....
    - fenwick67 9y ago
      Great link, thanks. These are the kind of systems that will really allow AI to take off.
  - fishcolorbrick 9y ago
    At my previous shop, it was normal to silently tolerate developers breaking the rules (not locking their stations when they stepped away, browsing unapproved sites, coming in late, leaving early)... until it was decided that they needed to be fired because they were rude to someone important, or etc... at that point they lost their privilege to act like a professional and their badge-access records were pulled and compared to their time-entry. The reason developers got fired is because of clear and well-defined rules... that were never enforced except when the decision to fire them for an arbitrary reason had already been made. I think HR-bot 5000 would treat employees more equitably by forcing employers to confront the inequity of some HR regulations. The same way self-driving cars "drive weird" because they stop at stop signs and drive the speed limit.
occultist_throw 9y ago
Well, where "Machine Learning" (aka: black box you can only get weights for a given question), one can train a machine to be racist, sexist, ageist, or whatever. The problem is that the end weight distribution is different than the GB's or TB's of training data. How do we know the training was fair and impartial? How did the trainers even know if it was? What biases crept in on this stage? Worse yet, what if the bias of the black box does denigrate black people... Say, we take in all pictures of convicted criminals- it's disproportionaly black. I would argue part of that is because of inherent policing biases, but that's embedded in "guilty" verdict. Who's at fault for this "bias"? Is there a fault? How do we detect, other than exhaustively? I'm eagerly awaiting for methods to "open up" ML black boxes and see what makes them tick. See their decision trees, their neural weights. I want to poke and prod to see what's behind those series of numbers like [.888271829 1.10999292992 37.999999921 1000.32 .73] . Right now, it's shove data in exhaustively and hope for the best. I don't particularly care for that way of analysis.