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Description: Researchers are developing a mathematical framework to evaluate the details of machine learning models and determine how well humans understand them.
Brief description: Researchers use deep description techniques to try to understand how machine learning models make decisions. Even if these explanations are correct, they are of no use if people cannot understand what they mean. MIT researchers have now developed a mathematical system for quantifying and evaluating information comprehension.
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Researchers use inference techniques to try to understand how machine learning models make decisions. Even if these explanations are correct, they are of no use if people cannot understand what they mean. MIT researchers have now developed a mathematical system for quantifying and evaluating information comprehension.
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Modern machine learning models, such as neural networks, are often called “black boxes” because they are so complex that even the researchers building them cannot fully understand how they make predictions.
To provide some knowledge, researchers use descriptive methods that seek to explain individual model decisions. For example, they could highlight words in a movie review that influenced the model’s assessment that the review was positive.
But these methods of explanation are useless if people cannot easily understand them or even misunderstand them. MIT researchers therefore developed a computational framework to formally quantify and evaluate the descriptive understanding of machine learning models. This can help reveal insights into the model’s behavior that may be missed if the researcher evaluates only a few isolated details in an attempt to understand the entire model.
“With this system, we can have a clear understanding of not only what we know about the model from this internal information, but more importantly what we don’t know about it,” said Yilun Zhu, a graduate student in electrical engineering and computer science. . student at the Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper introducing this system.
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Zhou’s co-authors include Marco Tullio Ribeiro, a principal investigator at Microsoft Research, and senior author Julie Shah, a professor of astronomy and astronomy and director of CSAIL’s Interactive Robots Group. The research will be presented at a conference of the North American Chapter of the Association for Computational Linguistics.
One way to understand a machine learning model is to find another model that replicates its predictions but uses open reasoning. However, recent neural network models are so complex that this approach usually fails. Instead, researchers choose to use internal explanations that focus on individual data. Often these annotations highlight words in the text to indicate their correspondence to a single prediction made by the model.
In effect, people then generalize these internal details to the overall behavior of the model. It can be found that a local descriptive approach containing positive words (such as “memorable,” “flawless,” or “lovely”) was the most influential when the model judged the movie review to be positive. Then they tend to assume that all positive terms have a positive effect on the model’s predictions, but that’s not always the case, Zhu says.
The researchers developed a system known as ExSum (short for descriptive summaries) that formalizes these types of requirements into testable rules using quantitative criteria. ExSum evaluates the rule over the entire dataset, not a single sample generated.
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Using a graphical user interface, one writes rules that can be modified, organized, and evaluated. For example, when looking at a model that learns to classify movie reviews as positive or negative, one might write a rule that says “negatives are negative,” meaning that “no,” “no,” and “nothing ” words have a negative effect. to movie review impressions.
Using ExSum, a user can see if a rule is performing using three specific metrics: coverage, validity, and severity. Coverage measures how commonly a rule is applied across the entire dataset. Validity refers to the percentage of individual examples that agree with a rule. Stringency describes how precise the law is; A valid law may be so general that it is not convenient to understand the example.
If a researcher is looking for a deeper understanding of how their model is doing, they can use ExSum to test specific ideas, Zhu says.
If he suspects that his model is sexist, he can create rules to say that male pronouns have positive contributions and female pronouns have negative contributions. If these rules have high validity, it means that they are generally correct and there is a possibility that the sample is biased.
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ExSum can also reveal unexpected information about the model’s behavior. For example, when assessing the ranking of movie reviews, the researchers were surprised to find that negative words contributed more clearly and strongly to model judgments than positive words. This may be because writers try to be polite and harsh when criticizing films, Zhu explains.
“In order to prove your understanding, you must in many cases evaluate these statements more rigorously. This kind of fine quality understanding has, as far as we know, never been revealed in previous work,” he says.
“Moving from local details to understanding the world was a big gap in the literature. ExSum is a good first step to fill that gap,” adds Ribeiro.
In the future, Zhu hopes to build on this work by extending the concept of comprehensibility to other parameters and types of information, such as pseudo-information (which shows how to modify the input to change the model’s predictions). Currently, they focus on feature representation techniques that define individual features of the model used to make a decision (eg, movie review words).
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Additionally, he wants to further improve the system and user interface so that people can quickly create rules. Writing rules can require hours of human involvement, and some level of human involvement is necessary because humans need to be able to understand the details, but AI support can simplify the process.
As he thinks about ExSum’s future, Zhou hopes their work highlights the need to change the way researchers think about machine learning model specifications.
“Before this job, if you have the right inside information, you’re done. You’ve reached the holy grail of describing your model. We recommend this additional dimension to ensure that this information is understandable. Clarity should be another measure for our evaluation. information,” says Zhu.
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