Understanding the Concepts: Explainability and Interpretability
Machine learning and artificial intelligence are becoming increasingly integral in critical fields like healthcare, finance, and legal systems. As these models grow in complexity, understanding how they make predictions is crucial. Explainability is the process of uncovering the ‘why’ behind a model’s decision – making. For instance, it could involve understanding how a model splits a particular node in a decision tree and the logic behind that split. Interpretability, on the other hand, is about translating these explanations and decisions for non – technical users. It helps data scientists understand elements like weights and coefficients in predictions and enables non – technical users to grasp how and based on what factors a model makes its decisions.
The Benefits of Improved Explainability and Interpretability
Improved Trust: Trust is fundamental when relying on ML and AI models. It is built over time through consistent positive interactions and transparency. Lack of these can erode trust. For example, in the case of a fully automated driving car, users need to trust its decision – making abilities to use it.
Enhanced Transparency and Collaboration: When the inner workings of a model are explainable, it establishes trust and accountability. It also promotes better collaboration with stakeholders and partners by allowing them to understand the model’s decision – making process and the intuition behind its rules and choices.
Effective Troubleshooting: In scenarios where a model’s output is unexpected, such as a loan application rejection, explainability helps in diagnosing issues. It allows us to examine the model’s predictions, identify influencing factors, and potentially discover biases that may be impacting decisions. This, in turn, enables efficient troubleshooting, monitoring, and continuous improvement of the model.
Popular Business Use Cases
Banking and Finance: In this highly regulated industry, accurate customer identification for loans and credit cards, as well as fraud prevention, are crucial. ML and AI models assist in these decisions. For example, if a model predicts the rejection of a high – credit – score customer’s loan application, model explanations can be used for risk analysis to understand the decision – making process and identify potential issues in the model.
Healthcare: ML/AI models are used to predict patient health outcomes. When predicting something as critical as cancer, healthcare professionals need transparency from the models. They expect clear explanations of predicted outcomes and the contributing factors, which helps in uncovering any hidden biases or vulnerabilities in the model.
Autonomous Vehicles: AI and ML models are at the core of autonomous vehicles. Since they involve public safety, building trust in these models is essential. Explainable AI (XAI) can provide real – time feedback on AI actions and decisions, investigate issues, and improve the models by identifying and eliminating biases.
Retail: AI and ML models guide decisions in product sales, inventory management, and marketing. Explainability helps in understanding model predictions related to product sales, demand, and the impact of marketing campaigns.
Tools for Model Explainability and Interpretability
LIME (Local Interpretable Model – Agnostic Explanations): LIME is model – agnostic and can work with various machine learning and deep learning models. It operates on the principle that a simple interpretable model can explain the workings of a complex one. It creates synthetic data using perturbed feature values from original observations to understand how a model makes predictions for a single observation. For example, in tabular data, it iteratively modifies feature values to create perturbed datasets.
SHAP (SHapely Additive exPlanations): SHAP is also model – agnostic and is based on the concept of Shapley values from cooperative game theory. It treats features as players in a coalition and fairly distributes the outcome gain among them, providing insights into feature contributions to model outcomes. It offers various plots like summary, dependence, force, and decision plots to understand model behavior at global and local levels.
Conclusion
LIME and SHAP are powerful tools for enhancing the explainability and interpretability of machine learning and AI models. LIME is excellent for local – level insights into individual predictions, while SHAP provides a more comprehensive view of feature importance at both global and local levels. However, higher model complexity often leads to better accuracy but reduced explainability. The choice between these tools depends on the specific requirements of accuracy versus interpretability for a given model.
