With the advent of artificial intelligence (AI), more and more organizations are using machine learning algorithms to make critical decisions that affect the lives of many people. However, the complexity and opacity of these algorithms can make it difficult to understand how such decisions are made. This is where the concepts of interpretability and explainability come into play.
In the world ofartificial intelligence (AI)and machine learning, interpretability and explainability are two terms often used to describe how understandable a model works.
What are Interpretability and Explainability?
- Interpretability: refers to the ability to understand the decision-making process of an AI model. An interpretable model is transparent in its operation and provides information about the relationships between inputs and outputs. An interpretable algorithm can be explained clearly and understandably by a human being. Interpretability is therefore important to ensure that users can understand and trust artificial intelligence models.
- Explainability: pertains to the ability to explain the decision-making process of an AI model in terms understandable to the end user. An explainable model provides a clear and intuitive explanation of the decisions made, enabling users to understand why the model produced a particular result. In other words, explainability focuses on why an algorithm made a specific decision and how that decision can be justified.
Differences between Interpretability and Explainability
Although interpretability and explainability are both important for understanding Artificial Intelligence models, there are some key differences between the two concepts:
- Level of detail: Interpretability focuses on understanding the inner workings of the models, while explainability focuses on explaining the decisions made. Consequently, interpretability requires a greater level of detail than explainability.
- Model complexity: More complex AI models, such as deep neural networks, can be difficult to interpret because of their intricate structure and the interactions between different parts of the model. In these cases, explainability may be more viable, as it focuses on explaining decisions rather than understanding the model itself.
- Communication: Interpretability concerns the understanding of the model by AI experts and researchers, whileexplainability is more focused on communicating model decisions to end users. As a result, explainability requires a simpler and more intuitive presentation of information.
The importance of Interpretability and Explainability
In general, interpretability and explainability are important because they provide insight into how decisions are made by machine learning algorithms. This is especially important in certain fields, such as medicine, where the choices made can have direct consequences on people's lives. Understanding how machine learning algorithms work can therefore help to ensure that the decisions made by these algorithms are right and that errors are minimized.
Interpretability and explainability are both essential to ensure that AI models are reliable, secure, and adhere to ethical principles appropriate to the context. Here are some of the reasons why these concepts are important:
Responsibility
An AI model that is interpretable and explainable enables users to understand the decision-making process and take into account the consequences of its decisions. This is crucial to ensure accountability and transparency in the use of AI.
Trust
Understanding AI models through interpretability and explainability can increase user confidence in decisions made by AI-based systems. When users understand how a model works and why it makes certain decisions, they are more likely to trust its recommendations.
Adaptation
An interpretable and explainable model allows developers to better understand model performance and identify any problems or areas requiring improvement. This facilitates adaptation and optimization of AI models over time.
Regulatory compliance
Compliance with data protection and AI ethics regulations often requires greater transparency in the decision-making process of AI models. Interpretability and explainability are essential to ensure that models comply with these requirements.
Bias reduction
Understanding how AI models work through interpretability and explainability enables the identification and reduction of bias in data and decision making. This can help ensure that AI models are more equitable and do not discriminate on the basis of sensitive characteristics, such as ethnicity, gender or disability.
Approaches to Improving Interpretability and Explainability
There are a number of methods and techniques that can be used to improve the interpretability and explainability of AI models, so as to make them more clear and functional.
Methods of visualization
Visualization of data and models can help simplify the understanding of how AI models work. For example, heat maps can be used to visualize the importance of different features in the decision-making process of a model.
Decomposition techniques
Decomposing the model into simpler components can make it easier to understand how it works. For example, decomposing a classification model into individual binary classifiers can make it easier to understand the model's decision-making process.
Explanations based on examples
Another approach to improving explainability is to provide explanations based on examples. This means showing the user examples of input similar to the one under consideration and explaining how the model made decisions in those cases.
Post-hoc methods
Post-hoc methods are techniques that are applied after the model has made a prediction to explain the decision-making process. For example, feature attribution can be used to identify which inputs had the greatest impact on the model decision.
Conclusions
Understanding the differences between these two terms and their importance is essential to ensure that Artificial Intelligence models are transparent, accountable, and compliant with regulations. Improving the interpretability and explainability of AI models leads to increased user confidence and facilitates their adoption in a wide range of industries and applications.
In addition, interpretability and explainability can also help simplify the decision to use AI by organizations. Managers may be more likely to adopt machine learning algorithms if they understand how they work and can justify their decisions.
XCALLY and the use of Artificial Intelligence
XCALLY, the omnichannel suite for contact centers., leverages artificial intelligence to improve the customer experience and simplify processes for handling user requests, thereby empowering customer care specialists to take charge of the most complex requests from customers. Data analysis and the use of methods based on interpretability and explainability enable our engineers to develop products that are increasingly useful to decision-making processes, ensuring their human-centered and ethical approach.
