Câu 45: RAI: Risk and Artificial Intelligence

A data scientist at a university is developing a model to predict how students will perform in various courses based on their performance in previous courses. The faculty has asked that a high degree of explainability be part of any model developed for this purpose. Which of the following statements correctly describes a technique that can be used to improve explainability?

1. A. LIME (locally interpretable model-agnostic explanations) approximates complex models with simpler models locally. — đáp án hiện tại
2. B. Principal Component Analysis reduces the dimensionality of the data by transforming the data into a set of orthogonal components.
3. C. Shapley values can be used to scale the data used in the model to ensure the features are all contributing to the model’s prediction.
4. D. LUCID (Locating Unfairness through Canonical Inverse Design) visualizes the activation patterns of different neural network layers.