Picking the Right Machine-Learning Explainer for Your Problem - Saliency Cards

Sunday - June 11 2023, 03:11 UTC - 1 year ago

MIT and IBM researchers developed saliency cards, a tool set designed to provide standardized documentation to machine-learning researchers and lay users alike, to help users choose the best saliency method for their particular task. Choosing the right method gives users a more accurate picture of how their model is behaving, thus better equipped to correctly interpret its predictions.

When machine-learning models are deployed in real-world situations, perhaps to flag potential disease in X-rays for a radiologist to review, human users need to know when to trust the model's predictions.But machine-learning models are so large and complex that even the scientists who design them don't understand exactly how the models make predictions. So, they create techniques known as saliency methods that seek to explain model behavior.

With new methods being released all the time, researchers from MIT and IBM Research created a tool to help users choose the best saliency method for their particular task. They developed saliency cards, which provide standardized documentation of how a method operates, including its strengths and weaknesses and explanations to help users interpret it correctly.

They hope that, armed with this information, users can deliberately select an appropriate saliency method for both the type of machine-learning model they are using and the task that model is performing, explains co-lead author Angie Boggust, a graduate student in electrical engineering and computer science at MIT and member of the Visualization Group of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

Interviews with AI researchers and experts from other fields revealed that the cards help people quickly conduct a side-by-side comparison of different methods and pick a task-appropriate technique. Choosing the right method gives users a more accurate picture of how their model is behaving, so they are better equipped to correctly interpret its predictions.

"Saliency cards are designed to give a quick, glanceable summary of a saliency method and also break it down into the most critical, human-centric attributes. They are really designed for everyone, from machine-learning researchers to lay users who are trying to understand which method to use and choose one for the first time," says Boggust.

Joining Boggust on the paper are co-lead author Harini Suresh, an MIT postdoc; Hendrik Strobelt, a senior research scientist at IBM Research; John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT; and senior author Arvind Satyanarayan, associate professor of computer science at MIT who leads the Visualization Group in CSAIL. The research will be presented at the ACM Conference on Fairness, Accountability, and Transparency.

Picking the right method .

The researchers have previously evaluated saliency methods using the notion of faithfulness. In this context, faithfulness captures how accurately a method reflects a model's decision-making process.

But faithfulness is not black-and-white, Boggust explains. A method might perform well under one test of faithfulness, but fail another. With so many saliency methods, and so many possible evaluations, users often settle on a method because it is popular or a colleague has used it.

However, picking the "wrong" method can have serious consequences. For instance, one saliency method, known as integrated gradients, compares the importance of features in an image to a meaningless baseline. The features with the largest differences are flagged as more important.