MIT researchers have developed tools to help data scientists make the features used in machine learning models more understandable to end users

Machine learning models are known to excel at a wide variety of tasks. To build trust in the AI ​​process, you need to understand how these models work. However, researchers still don’t quite understand how AI/ML models use certain aspects or come to certain conclusions due to the complexity of the functions and algorithms used to train these models.

Recent research by the MIT team creates a taxonomy to help developers in various places create features that their target audience can more easily understand. In their paper, “The Need for Interpretable Features: Motivation and Taxonomy,” they identify the traits that make features more interpretable to create the taxonomy. They did this for five different user types: from artificial intelligence professionals to those who can influence the prediction of a machine learning model. They also offer advice on how developers can make features more accessible to the general public.

Machine learning models use functions as input variables. The features are often chosen to ensure greater accuracy of the model rather than whether a decision maker can interpret them.

The team found that some features, such as the trend of a patient’s heart rate over time, were presented as aggregated values ​​by machine learning models used to predict the risk that a patient will experience complications after heart surgery. . Clinicians did not know how the characteristics obtained in this way were calculated, despite being ‘model ready’.

Many scientists, on the other hand, value aggregated characteristics. For example, instead of a feature like “number of posts a student has made on discussion forums,” they prefer to have relevant features aggregated and tagged with words they recognize, such as “participation.”

Their lead researcher claims that there are many levels of interpretability, and this is an important driving factor behind their work. They describe which attributes are likely to be most important to particular users and specify attributes that can make attributes more or less interpretable by different decision makers.

For example, machine learning developers can prioritize predictive and compliant features, improving model performance. On the other hand, many people value human-word functions (which are described in a natural way to users) that are understandable and better suited to decision makers with no previous experience with machine learning.

When creating interpretable features, it is important to understand the level to which they are interpretable. According to them, depending on the domain, you may not need all levels.

The researchers also propose feature engineering methodologies that developers can use to make features more understandable to a particular audience.

For machine learning models to process the data, data scientists use aggregation and normalization techniques. In many cases, it is nearly impossible for the average person to interpret these changes. Furthermore, most models cannot process categorical data without first converting it into a numerical code.

They note that it may be necessary to undo some of that encoding to produce interpretable attributes. Furthermore, many fields have a minimal trade-off between interpretable features and model accuracy. For example, the researchers mention in one of their papers that they stuck with the features that met their standards for interpretability while retraining the model for child welfare screeners. The results showed that the model’s performance drop was essentially non-existent.

Their work will enable a model developer to more effectively manage complex function transformations and produce explanations for human-centric machine learning models. In addition, this new system will translate algorithms created to explain model-ready datasets into formats that decision makers can understand.

They believe that their research would encourage model developers to incorporate interpretable elements early rather than focusing on later explainability.

This Article is written as a summary article by Marktechpost Staff based on the research paper 'The Need for Interpretable Features: Motivation and Taxonomy'. All credit for this research goes to researchers on this project. Checkout the paper and blog post.

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