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From the article: "... the researchers are concerned that AI could end up perpetuating racial biases. They are especially concerned that they could not figure o

From the article: "... the researchers are concerned that AI could end up perpetuating racial biases. They are especially concerned that they could not figure out precisely how the machine-learning models were able to identify race, even from heavily corrupted and low-resolution images." What is the closest thing to a "stack trace" to debug and understand AI neural network output? I'm new to this and curious of where to start looking to further understand how one would reverse engineer the logic or reasoning behind AI output.

joeld42 5y ago
This is a good start: https://distill.pub/2017/feature-visualization/ https://distill.pub/2017/feature-visualization/
marcyb5st 5y ago
With very deep models based on Convolution (CNNs, the article is talking about AI on a computer vision problem and CNNs are the standard for these problems) you loose almost all explainability. There might be ways to partially mitigate that like adding additional "heads" to the model to output some debugging-like data, or run the image in patches and see where the model is more confident about a particular metric of interest. Both these options are not optimal as you still don't see the inner workings of the model in an interpretable way. Transformers models [1], on the other hand, are the de-facto standard for Natural Language Processing/Understanding problems and recent studies [2] show that they can be applied to computer vision tasks. By construction, transformers let you access the so called "attention heads", which carry a good deal of information about what the model believes is important about the input. So that might be a promising route for some explainability in computer vision tasks, but we are quite a bit far away from fully understanding what is going on inside a deep learning model. [1] https://arxiv.org/abs/1706.03762 https://arxiv.org/abs/1706.03762 [2] https://arxiv.org/abs/2010.11929 https://arxiv.org/abs/2010.11929
- malux85 5y ago
  I have been working with CNN's for 5-6 years now, and every time I've needed explanations I used class activation maps http://cnnlocalization.csail.mit.edu/ http://cnnlocalization.csail.mit.edu/ Can this help?
0-_-0 5y ago
Masking out parts of the image and monitoring how each part changes the prediction. This lets you porduce and "importance map" about which parts of the image contribute more to the prediction.
axelroze 5y ago
The Artificial Neural Network is essentially just a huge set of formulas involving millions and sometimes even billions of values. Debugging in conventional software engineering sense of having a breakpoint and looking at values does not work. The current state of the art for analysis is ShAP: - https://github.com/slundberg/shap https://github.com/slundberg/shap ShAP is primarily an instance based explainer (one image = one explanation) but if you run it over multiple instances it is possible to gather global model insights on the data. The internals of the model are still quite unexplainable compared to decision trees or anything a human can code (horrible code aside). There is a group at ETH doing work on adversarial attacks: - https://www.sri.inf.ethz.ch/publications/ https://www.sri.inf.ethz.ch/publications/ While not directly related to explainability their work is on providing bounds on how much can corruption of the input still provide valid output. Very interesting and practically relevant as well. Finally there is also common sense. If race was a large factor in prediction then the model will implicitly learn to predict races. I am not in medicine and do not know how much it is but if it is then the only way to not learn race prediction is to make race not correlated to the targets.

joeld42 5y ago

This is a good start: https://distill.pub/2017/feature-visualization/ https://distill.pub/2017/feature-visualization/

marcyb5st 5y ago

With very deep models based on Convolution (CNNs, the article is talking about AI on a computer vision problem and CNNs are the standard for these problems) you loose almost all explainability. There might be ways to partially mitigate that like adding additional "heads" to the model to output some debugging-like data, or run the image in patches and see where the model is more confident about a particular metric of interest. Both these options are not optimal as you still don't see the inner workings of the model in an interpretable way. Transformers models [1], on the other hand, are the de-facto standard for Natural Language Processing/Understanding problems and recent studies [2] show that they can be applied to computer vision tasks. By construction, transformers let you access the so called "attention heads", which carry a good deal of information about what the model believes is important about the input. So that might be a promising route for some explainability in computer vision tasks, but we are quite a bit far away from fully understanding what is going on inside a deep learning model. [1] https://arxiv.org/abs/1706.03762 https://arxiv.org/abs/1706.03762 [2] https://arxiv.org/abs/2010.11929 https://arxiv.org/abs/2010.11929

malux85 5y ago
I have been working with CNN's for 5-6 years now, and every time I've needed explanations I used class activation maps http://cnnlocalization.csail.mit.edu/ http://cnnlocalization.csail.mit.edu/ Can this help?

0-_-0 5y ago

Masking out parts of the image and monitoring how each part changes the prediction. This lets you porduce and "importance map" about which parts of the image contribute more to the prediction.

axelroze 5y ago

The Artificial Neural Network is essentially just a huge set of formulas involving millions and sometimes even billions of values. Debugging in conventional software engineering sense of having a breakpoint and looking at values does not work. The current state of the art for analysis is ShAP: - https://github.com/slundberg/shap https://github.com/slundberg/shap ShAP is primarily an instance based explainer (one image = one explanation) but if you run it over multiple instances it is possible to gather global model insights on the data. The internals of the model are still quite unexplainable compared to decision trees or anything a human can code (horrible code aside). There is a group at ETH doing work on adversarial attacks: - https://www.sri.inf.ethz.ch/publications/ https://www.sri.inf.ethz.ch/publications/ While not directly related to explainability their work is on providing bounds on how much can corruption of the input still provide valid output. Very interesting and practically relevant as well. Finally there is also common sense. If race was a large factor in prediction then the model will implicitly learn to predict races. I am not in medicine and do not know how much it is but if it is then the only way to not learn race prediction is to make race not correlated to the targets.