Explainability

In the context of artificial intelligence (AI) and machine learning, explainability refers to the ability to understand and explain, clearly and in detail, the way in which a model machine learning generates predictions, makes decisions or provides recommendations. Explainability is a fundamental property of AI systems, as it ensures the transparency and reliability of the model. 

The importance of explainability 

Explainability is essential for understanding the strengths and limitations of an AI model, improving its performance, detecting potential errors, and preventing issues relating to ethics and fairness in the decision-making process. Furthermore, explainability is a legal and regulatory requirement in some cases, such as in the financial sector and in medicine, where automated decisions can have a significant impact on people. 

Explainability techniques 

There are several techniques and methods for improving explainability in machine learning; some of these are: 

• Model interpretability: This approach involves using intrinsically interpretable machine learning models, such as decision trees and linear regression models, which make it easy to understand how predictions are generated. 

• Local explanations: These techniques make it possible to explain a model’s predictions in a specific case or for a particular data instance. Examples include LIME (Local Interpretable Model Explanations) and SHAP (Shapley Values). 

• General explanations: They are used to explain how the model works as a whole, by analysing the relationships between the input features and the predictions generated. Examples include association rule methods and self-organising maps. 

Challenges of explainability 

Despite the importance of explainability in machine learning, there are several challenges that need to be addressed: 

• Complexity: Some AI models, such as deep neural networks and deep learning models, can be extremely complex and difficult to interpret due to the large number of parameters and the non-linear nature of their transformations. 

• Data quality: Explainability is closely linked to the quality of the data used to train the model. If the data contains bias or errors, the model’s explanation may be misleading or incorrect. 

• Balancing explainability and performance: Sometimes, there is a trade-off between explainability and model performance. The most interpretable models are not always the ones that achieve the best results in terms of accuracy or efficiency. 

Tools and frameworks for explainability 

There are numerous tools and software libraries that help researchers and professionals improve the explainability of their AI models. Some examples include: 

• Scikit-learn: Bookshop a Python machine learning library that provides methods and tools for model interpretability, such as partial dependency plots and feature permutation. 

• LIME: A tool that generates interpretable local explanations for models, regardless of the type or complexity of the model. 

• Shapley: A library based on the Shapley values method, which enables the importance of features in the model’s predictions to be calculated. 

• Explainable AI Toolkit (XAI): Open-source tools from IBM Research to improve explainability in AI models, including methods for interpretability and data visualisation. 

Explainability in different fields 

Explainability is a key concern in various fields that utilise AI, including: 

• Medicine: AI models are increasingly being used in the diagnosis and treatment of diseases. Explainability is essential to ensure that doctors and patients understand and trust the model’s recommendations. 

• Finance: AI is used in banking, investment and insurance applications. Explainability is necessary to ensure the transparency and reliability of models, as well as to comply with regulations. 

• Law: AI models are increasingly being used in legal decision-making. Explainability is essential to ensuring the fairness and impartiality of legal proceedings. 

Research into explainability 

Explainability remains an active area of research within the AI scientific community. Some areas of research include: 

• Interpretability of deep neural networks: To investigate new methods for interpreting and explaining the predictions of complex deep neural networks. 

• Explainability in unsupervised learning: To develop techniques for explaining the structure and organisation of unlabelled data. 

• Standards and regulations: To define standards and regulations for explainability in AI, particularly in sensitive areas such as medicine and finance.