4 ms·

> 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 und

by 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).

protomyth 9y ago
Ok, I get the training and verifying via test data sets, but I really don't get how a piece of software cannot be explained. It runs on a computer. It executes instructions that have to be visible in a trace. Someone had to write and debug the initial code. The data for the customer flows through the system, so how the heck can it be a black box? This is not mystical, its computer instructions. I can accept that data scientists have built software that doesn't output enough information to model the decisions, but I cannot accept a artificial system cannot be trace to show the decisions. If this is the type of software that is going to be running anything that can kill (e.g. autonomous cars) or fry (e.g. medical equipment) a human then I hope someone makes it illegal as heck until the model can be reversed in every case. How in the heck do developer / data scientists think they can get away with a black box with no accountability?
- bobcostas55 9y ago
  If a neural network has let's say 50 million parameters, it doesn't matter if you can trace any of the calculations, you're never going to actually understand how the model operates. Sometimes it's possible to extract a sort of conceptual understanding in some cases, eg you might say that this layer performs edge detection or whatever. But that's not much of an explanation. They do it because it works. You trust your Uber driver to get you to your destination even though you have no idea how his neurons do it. People are going to trust machine intelligence in the same way.
  - protomyth 9y ago
    People are going to trust machine intelligence in the same way. Given the xenophobic history of the human race, I have some severe doubts about that. Frankly, the first data scientist who ends up in court because the automated car decided to kill some kid on the sidewalk[1] to keep the death count down is going to find out real quick what a jury thinks of machine intelligence. Thinking about it, I can see the black box in a car having to record the last minute of instructions run by the car's cpu. The whole idea of unknowable isn't exactly going to sit well with the NTSB. I can truly see this if data scientists testify in a Congressional Hearing that they don't understand how their creation came to its decision. I guess I am in awe of folks who do not have tools to figure out what model all the learning has built. Where is the DTrace for ML? If your neural network has 50 million parameters then how the heck did the data scientist have a data set to teach it from that is in anyway complete enough to trust it? I can just not see how it ethically can be unleashed upon people in a final go / no go decision affecting people's lives or livelihood. After reading this thread, I now hope people who are rejected for loans ask for an actual explanation and the exact manner the decision for their rejection was reached. 1) https://www.usatoday.com/story/money/cars/2017/11/23/self-driving-cars-programmed-decide-who-dies-crash/891493001/ https://www.usatoday.com/story/money/cars/2017/11/23/self-dr...

by Aeolos 9y ago

protomyth 9y ago

Ok, I get the training and verifying via test data sets, but I really don't get how a piece of software cannot be explained. It runs on a computer. It executes instructions that have to be visible in a trace. Someone had to write and debug the initial code. The data for the customer flows through the system, so how the heck can it be a black box? This is not mystical, its computer instructions. I can accept that data scientists have built software that doesn't output enough information to model the decisions, but I cannot accept a artificial system cannot be trace to show the decisions. If this is the type of software that is going to be running anything that can kill (e.g. autonomous cars) or fry (e.g. medical equipment) a human then I hope someone makes it illegal as heck until the model can be reversed in every case. How in the heck do developer / data scientists think they can get away with a black box with no accountability?

bobcostas55 9y ago
If a neural network has let's say 50 million parameters, it doesn't matter if you can trace any of the calculations, you're never going to actually understand how the model operates. Sometimes it's possible to extract a sort of conceptual understanding in some cases, eg you might say that this layer performs edge detection or whatever. But that's not much of an explanation. They do it because it works. You trust your Uber driver to get you to your destination even though you have no idea how his neurons do it. People are going to trust machine intelligence in the same way.
- protomyth 9y ago
  People are going to trust machine intelligence in the same way. Given the xenophobic history of the human race, I have some severe doubts about that. Frankly, the first data scientist who ends up in court because the automated car decided to kill some kid on the sidewalk[1] to keep the death count down is going to find out real quick what a jury thinks of machine intelligence. Thinking about it, I can see the black box in a car having to record the last minute of instructions run by the car's cpu. The whole idea of unknowable isn't exactly going to sit well with the NTSB. I can truly see this if data scientists testify in a Congressional Hearing that they don't understand how their creation came to its decision. I guess I am in awe of folks who do not have tools to figure out what model all the learning has built. Where is the DTrace for ML? If your neural network has 50 million parameters then how the heck did the data scientist have a data set to teach it from that is in anyway complete enough to trust it? I can just not see how it ethically can be unleashed upon people in a final go / no go decision affecting people's lives or livelihood. After reading this thread, I now hope people who are rejected for loans ask for an actual explanation and the exact manner the decision for their rejection was reached. 1) https://www.usatoday.com/story/money/cars/2017/11/23/self-driving-cars-programmed-decide-who-dies-crash/891493001/ https://www.usatoday.com/story/money/cars/2017/11/23/self-dr...