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This could be a real problem in their respective careers (or not). Here's the analogy: scientists who use statistical tools as blackboxes are the ones responsib

by shas3 11y ago

This could be a real problem in their respective careers (or not). Here's the analogy: scientists who use statistical tools as blackboxes are the ones responsible for the whole problem with misusing p-values. Similarly, poor intuition and training in machine learning leads to the blackbox mentality and consequently, problems with building working systems.

forgetsusername 11y ago
That's not really an analogy, it's an assertion without a shred of evidence. I'm torn about the blackbox thing. On one hand, it's important to understand the underpinnings of a model. On the other, we utilize a multitude of things in our daily lives of which we have no fundamental understanding; that's abstraction in a nutshell.
- hiddencost 11y ago
  Machine learning gets kinda scary, though. For one example, discrimination with ML is super easy. Check out fatml.org for instance. Also, with ML it's really easyfor an amateur to over fit like crazy and draw spurious conclusions due to poor methods. People think they have an intelligence when they instead have very finicky tools Edit: another pointer here https://algorithmicfairness.wordpress.com https://algorithmicfairness.wordpress.com
- shas3 11y ago
  There is a crucial distinction between the multitude of things we utilize in our daily lives and machine learning/high-dimensional data analysis: we aren't equipped to intuit the workings of high-dimensional advanced-math statistical inference in the same way that we can intuit the workings of say, a water pump, or simple arithmetic on Excel, or simple database systems, unless we are appropriately trained in the relevant math and science. Some examples: blackbox application of classifiers (e.g. WEKA gui as used by some for data exploration) can ignore parameter optimization, unbalanced sets, parsimony in features, dimensionality reduction, etc. etc.
GFK_of_xmaspast 11y ago
Scientists are also domain experts tho, but bootcamp-style practitioners won't be.

by shas3 11y ago

forgetsusername 11y ago

That's not really an analogy, it's an assertion without a shred of evidence. I'm torn about the blackbox thing. On one hand, it's important to understand the underpinnings of a model. On the other, we utilize a multitude of things in our daily lives of which we have no fundamental understanding; that's abstraction in a nutshell.

hiddencost 11y ago
Machine learning gets kinda scary, though. For one example, discrimination with ML is super easy. Check out fatml.org for instance. Also, with ML it's really easyfor an amateur to over fit like crazy and draw spurious conclusions due to poor methods. People think they have an intelligence when they instead have very finicky tools Edit: another pointer here https://algorithmicfairness.wordpress.com https://algorithmicfairness.wordpress.com
shas3 11y ago
There is a crucial distinction between the multitude of things we utilize in our daily lives and machine learning/high-dimensional data analysis: we aren't equipped to intuit the workings of high-dimensional advanced-math statistical inference in the same way that we can intuit the workings of say, a water pump, or simple arithmetic on Excel, or simple database systems, unless we are appropriately trained in the relevant math and science. Some examples: blackbox application of classifiers (e.g. WEKA gui as used by some for data exploration) can ignore parameter optimization, unbalanced sets, parsimony in features, dimensionality reduction, etc. etc.

GFK_of_xmaspast 11y ago

Scientists are also domain experts tho, but bootcamp-style practitioners won't be.