Interpretation in AI refers to the explanation or understanding of how an AI model made a particular decision or prediction. It’s important for transparency and allows users to trust in a model’s outputs.


Imagine you fill in a quiz where you guess the weight of an apple in a mystery box. You win the quiz, but you don’t know why your guess was the closest. Now, let’s say the quiz master tells you how he picked the apple (say, it was the smallest apple). Now you can understand why your guess won; this understanding process is what we call “interpretation” in AI – understanding how the AI made its “guess.”

In-depth explanation

In machine learning, interpretation is a valuable concept that aims to comprehend how an AI model makes decisions. This task can be challenging due to the complexity of many models, particularly with deep learning, where they are often referred to as ‘black-box’ models.

Interpretation methods can be broadly grouped into two categories: transparent box interpretation and post-hoc interpretation.

Transparent box interpretation involves using algorithms with interpretable properties by design. An example of this is a decision tree algorithm, where the decision rules can be explicitly outlined and analyzed.

On the other hand, post-hoc interpretation is employed when the model is inherently opaque (as with many deep learning models), and so interpreting the model requires external analysis. This usually involves techniques that create interpretable versions of the model’s behavior or highlight the contribution of each input feature to the final prediction. Techniques in this category include permutation feature importance, local interpretable model-agnostic explanations (LIME), and SHAP values.

Interpretation is key to building trust in AI models. If users can comprehend what factors are contributing to an AI’s decisions, they are more likely to trust and effectively use the technology. Further, it is crucial to ensuring ethical considerations as it allows for identifying and remedying potential biases present in a model.

Overall, interpretation is an indispensable tool that researchers and practitioners use to better understand and improve their AI models, fostering trust, ethical fairness, and ultimately enhancing the model’s performance.

Deep Learning, Decision Trees, Feature Importance, Post-hoc Interpretation, Bias, Machine Learning (ML), Ethics, LIME, SHAP, Model Transparency.