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"Ethics in AI" have deep implications for improving our AI systems. For example, bias in training data has an outcome of the performance of a system. Ethics in

by mempko 6y ago

"Ethics in AI" have deep implications for improving our AI systems. For example, bias in training data has an outcome of the performance of a system. Ethics in AI movements looks deeper into this process and does improve performance in AI systems. Also because AI deeply interacts with human systems today at a large scale (think youtube recommendation algorithm) it will also have a large affect on society. So not sure what "political" agenda there is there except acknowledging the obvious.

SpicyLemonZest 6y ago
Google's AI ethics board in 2019, for example, was killed for explicitly political reasons. Many AI ethics commentators, including two of the board members, indicated that Kay Coles James should not be permitted to participate in AI ethics discussions because of her politics.
vorhemus 6y ago
If attributes such as age or gender are strong features for certain dependent variables that improve the predictive performance of a model, then it must be allowed to use these variables. For example, it is well known that colorectal cancer is more prevalent in men over the age of 50 - so a statistical model used to allocate free colorectal exams would favor old men. Is that discriminatory? Likewise, for any given variable, one will find certain features that favor men or women, young or old; finding these dependencies is precisely the point behind statistical models.
- monocasa 6y ago
  On the other hand, if a model fails on certain populations because not enough training data including them was input because they're historically seen as a less important subgroup, then you've simply encoded your societal biases in your model. Understanding that difference and pointing out problem spots like that is a great job for an ethical AI researcher.
- mempko 6y ago
  As you know, most AI is curve fitting or representational learning. So the question is, is the statistical distribution your model learning a static one, bound by natural laws like physics or biology, or are they human systems with changing distributions where the machine itself can cause effects and impacts. Your example is of the former, while the ML model predicting 'success at a job' or 'credit worthyness' falls in the latter category. The latter category is harder in every way and has ethical concerns because it necessarily can change (or usually keeps the same) the man made social systems.
McScroogy 6y ago
As if high level AI researchers would not be aware of the issue of bias. The whole field is about studying bias, really.

by mempko 6y ago

SpicyLemonZest 6y ago

Google's AI ethics board in 2019, for example, was killed for explicitly political reasons. Many AI ethics commentators, including two of the board members, indicated that Kay Coles James should not be permitted to participate in AI ethics discussions because of her politics.

vorhemus 6y ago

If attributes such as age or gender are strong features for certain dependent variables that improve the predictive performance of a model, then it must be allowed to use these variables. For example, it is well known that colorectal cancer is more prevalent in men over the age of 50 - so a statistical model used to allocate free colorectal exams would favor old men. Is that discriminatory? Likewise, for any given variable, one will find certain features that favor men or women, young or old; finding these dependencies is precisely the point behind statistical models.

monocasa 6y ago
On the other hand, if a model fails on certain populations because not enough training data including them was input because they're historically seen as a less important subgroup, then you've simply encoded your societal biases in your model. Understanding that difference and pointing out problem spots like that is a great job for an ethical AI researcher.
mempko 6y ago
As you know, most AI is curve fitting or representational learning. So the question is, is the statistical distribution your model learning a static one, bound by natural laws like physics or biology, or are they human systems with changing distributions where the machine itself can cause effects and impacts. Your example is of the former, while the ML model predicting 'success at a job' or 'credit worthyness' falls in the latter category. The latter category is harder in every way and has ethical concerns because it necessarily can change (or usually keeps the same) the man made social systems.

McScroogy 6y ago

As if high level AI researchers would not be aware of the issue of bias. The whole field is about studying bias, really.