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The point of interpretability in ML is that the "reasons" that model arrived at a decision/classification/etc are incomprehensible (and, given e.g. the existenc

The point of interpretability in ML is that the "reasons" that model arrived at a decision/classification/etc are incomprehensible (and, given e.g. the existence of adversarial examples in image classification, this should be expected -- no reasonable answer exists for "why" the model thought this static was a cat).

shawn-butler 9y ago
It's not magic. It amazes me how many "data scientists" can't explain the basic mathematics behind their modelling. If the outcome of algorithm/model can't be explained by its regulators then it shouldn't be making any decisions of any significance or importance to human lives. When engineers fail to address cracking in a bridge and people die when it falls, humans are held to account and explain why this decision was so. If machine learning statistical models can't do the same, they better not be used to scan any bridges for structural cracking where I live and vote.
philipodonnell 9y ago
I have heard approaches described that train a simpler ML algorithm to predict why a complex ML algorithm made a decision. Decision-tree models predicting neural net models. Paypal or Square I believe.
- vinn124 9y ago
  how does this solve anything? if a simple decision tree could predict the outputs of more complex deep nets, why not use the decision tree in the first place? also, what do you do when a decision tree isnt powerful enough, as in the case of many interesting problems such as speech, computer vision, etc.
  - philipodonnell 9y ago
    The uses cases for this were around fraud detection systems where you need to provide a reason for the flag. I don't have to know the complexities of how all the factors are interacting within a neural net to be able to tell someone that if they increased their credit score by 100 points, they'd be approved.
protomyth 9y ago
Hold up, data scientists don't know why their ML is deciding things and companies are using it to decide important things in people's lives? Sounds like it should be banned until companies can actually explain the decisions and some data scientists need to take an ethics class.
- bobcostas55 9y ago
  >until companies can actually explain the decisions That's almost certainly never going to happen. Our most accurate models are unintelligible, and our most intelligible models are inaccurate. There's a trade-off here and without some sort of magic I don't see us transcending that trade-off.
  - sgt101 9y ago
    What's the uplift between your super accurate model and 1-r? What's the uplift vs a 50 node decision tree? Also accuracy is hard to talk about if you don't have the whole distribution you are estimating from; which generally we don't... Many highly accurate models (in the sense of working on the test set and n-fold xvalidation) underperform in production.
  - protomyth 9y ago
    This is where I don't understand. If the data scientists don't know the why of the decision making and the software cannot explain itself then how do they understand that it is accurate? Going further, how in the heck do they justify using something like this on credit applications or anything else that can adversely affect their fellow humans? The ethics of such a lack of knowledge should give pause and frankly I hope they get sued if this is the truth of the matter. That is monstrous.
    - Aeolos 9y ago
      > This is where I don't understand. If the data scientists don't know the why of the decision making and the software cannot explain itself then how do they understand that it is accurate? They understand that by evaluating the model on a "test dataset" that is, hopefully, representative of the real-world. This does allow you can explain why a given model makes the decisions it makes - it only allows you to understand how well it performs on the data you feed it. Using an inaccurate or biased model to detect assign credit ratings is indeed immoral. This is orthogonal to interpretability: you can have a model that is both inaccurate and interpretable (i.e. it's wrong and you can say why it's wrong); you can also have a model that is highly accurate but not interpretable (i.e. it works, but you cannot explain why).

shawn-butler 9y ago

It's not magic. It amazes me how many "data scientists" can't explain the basic mathematics behind their modelling. If the outcome of algorithm/model can't be explained by its regulators then it shouldn't be making any decisions of any significance or importance to human lives. When engineers fail to address cracking in a bridge and people die when it falls, humans are held to account and explain why this decision was so. If machine learning statistical models can't do the same, they better not be used to scan any bridges for structural cracking where I live and vote.

philipodonnell 9y ago

I have heard approaches described that train a simpler ML algorithm to predict why a complex ML algorithm made a decision. Decision-tree models predicting neural net models. Paypal or Square I believe.

vinn124 9y ago
how does this solve anything? if a simple decision tree could predict the outputs of more complex deep nets, why not use the decision tree in the first place? also, what do you do when a decision tree isnt powerful enough, as in the case of many interesting problems such as speech, computer vision, etc.
- philipodonnell 9y ago
  The uses cases for this were around fraud detection systems where you need to provide a reason for the flag. I don't have to know the complexities of how all the factors are interacting within a neural net to be able to tell someone that if they increased their credit score by 100 points, they'd be approved.

protomyth 9y ago

Hold up, data scientists don't know why their ML is deciding things and companies are using it to decide important things in people's lives? Sounds like it should be banned until companies can actually explain the decisions and some data scientists need to take an ethics class.

bobcostas55 9y ago
>until companies can actually explain the decisions That's almost certainly never going to happen. Our most accurate models are unintelligible, and our most intelligible models are inaccurate. There's a trade-off here and without some sort of magic I don't see us transcending that trade-off.
- sgt101 9y ago
  What's the uplift between your super accurate model and 1-r? What's the uplift vs a 50 node decision tree? Also accuracy is hard to talk about if you don't have the whole distribution you are estimating from; which generally we don't... Many highly accurate models (in the sense of working on the test set and n-fold xvalidation) underperform in production.
- protomyth 9y ago
  This is where I don't understand. If the data scientists don't know the why of the decision making and the software cannot explain itself then how do they understand that it is accurate? Going further, how in the heck do they justify using something like this on credit applications or anything else that can adversely affect their fellow humans? The ethics of such a lack of knowledge should give pause and frankly I hope they get sued if this is the truth of the matter. That is monstrous.
  - Aeolos 9y ago
    > This is where I don't understand. If the data scientists don't know the why of the decision making and the software cannot explain itself then how do they understand that it is accurate? They understand that by evaluating the model on a "test dataset" that is, hopefully, representative of the real-world. This does allow you can explain why a given model makes the decisions it makes - it only allows you to understand how well it performs on the data you feed it. Using an inaccurate or biased model to detect assign credit ratings is indeed immoral. This is orthogonal to interpretability: you can have a model that is both inaccurate and interpretable (i.e. it's wrong and you can say why it's wrong); you can also have a model that is highly accurate but not interpretable (i.e. it works, but you cannot explain why).