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What is interpretation?

by Stephen M. Walker II, Co-Founder / CEO

What is interpretation?

Interpretation refers to the process of understanding or making sense of data, code, or a computer program's behavior. It involves translating abstract concepts into concrete terms that can be easily comprehended by humans. In software development and programming, interpretation is used in various contexts such as debugging, analyzing performance, and assessing algorithmic complexity. The goal of interpretation is to provide insights into the inner workings of a program or system, enabling developers to improve its functionality, efficiency, and reliability.

What is interpretation in AI?

In the context of artificial intelligence (AI), interpretation refers to the process of understanding and explaining the decisions made by an AI model. It's a key aspect of explainable AI (XAI), which focuses on creating transparent models that provide clear and understandable explanations for their decisions.

Interpretability is the degree to which a human can understand the cause of a decision made by an AI model. It involves understanding the what, why, and how of the model's decisions. The three most important aspects of model interpretation are transparency, the ability to question, and the ease of understanding.

Model interpretation is crucial for several reasons. It helps ensure fairness, accountability, and transparency, which can increase human confidence in using AI models. It also allows users to understand the reasoning behind a model's predictions, which can be particularly important in fields where incorrect or strange predictions could have serious consequences.

There are various techniques and tools available for interpreting AI models. These include feature importance analysis, which identifies influential variables that significantly impact the model's predictions, and tools like SHAP (SHapley Additive exPlanations), which estimates feature importance and provides model-agnostic explanations.

What is the difference between interpretability and explainability in AI?

Interpretability and explainability are two related but distinct concepts.

Interpretability refers to the ability to understand the decision-making process of an AI model. It focuses on understanding the inner workings of the models, and requires a greater level of detail than explainability. An interpretable model is transparent in its operation and provides information about how it makes its decisions. It's about understanding how the model itself works and how it makes its decisions. Interpretability means that the cause and effect can be determined. If a model can take the inputs, and routinely get the same outputs, the model is interpretable.

On the other hand, explainability pertains to the ability to explain the decision-making process of an AI model in terms that humans can understand. An explainable model provides a clear and intuitive explanation of the decisions made, enabling users to understand why the model produced a particular result. Explainability is about being able to quite literally explain what is happening.

While both interpretability and explainability aim to make AI and ML models more understandable to humans, they do so in different ways. Interpretability focuses on understanding the inner workings of the model, while explainability focuses on explaining the decisions made by the model in a way that humans can understand.

What are some examples of interpretable machine learning models?

In the field of machine learning, some models are inherently interpretable, meaning that their decision-making process can be easily understood by humans. Here are a few examples of such models:

  1. Linear Regression Models — These models predict the output by establishing a linear relationship between the input variables and the output. The coefficients of the model represent the change in the output variable for a one-unit change in the input variable, making it easy to understand the impact of each feature on the prediction.

  2. Logistic Regression Models — Logistic regression is used for binary classification problems. It estimates the probability that a given input point belongs to a certain class. Similar to linear regression, the coefficients in a logistic regression model represent the change in the log-odds of the output variable for a one-unit change in the input variable, providing an interpretable understanding of the features.

  3. Decision Trees — Decision trees make decisions by splitting the data based on the values of the input features. Each node in the tree represents a feature in the dataset, each branch represents a decision rule, and each leaf node represents an outcome. The decision-making process of a decision tree is very intuitive and easy to follow, making it one of the most interpretable machine learning models.

  4. RuleFit — RuleFit is a model that uses decision rules derived from decision trees in a linear model. It combines the interpretability of decision trees with the predictive power of linear models. The model provides a set of rules, and each rule's influence on the prediction can be easily understood.

These models are considered interpretable because they provide clear insights into their decision-making process, allowing humans to understand the cause and effect in the system. However, it's important to note that the interpretability of a model can sometimes be a trade-off with its predictive power. More complex models like neural networks can often provide better predictions, but their decision-making process is much harder to interpret.

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