10  Interpretability and Explainability

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10.1 Guidotti, Riccardo

📖 Developed LIME (Local Interpretable Model-Agnostic Explanations), which approximates complex models by simpler ones, providing local explanations for predictions.

“Complex models can be approximated by simpler ones to provide local explanations for predictions.”

— Guidotti, Riccardo, Machine Learning Journal

“LIME (Local Interpretable Model-Agnostic Explanations) is a technique that can be used to explain the predictions of any machine learning model.”

— Guidotti, Riccardo, Machine Learning Journal

“Local explanations can help us to understand how a model makes predictions and can be used to identify potential errors in the model.”

— Guidotti, Riccardo, Machine Learning Journal

10.2 Ribeiro, Marco Tulio

📖 Proposed LIME (Local Interpretable Model-Agnostic Explanations), enabling the explanation of predictions of any complex model with local surrogate models.

“The accuracy of a model is a necessary but insufficient condition for a model being understandable.”

— Ribeiro, Marco Tulio, Journal of Machine Learning Research

In order for a model to be understandable, it is not enough for it to be accurate. Understanding requires the model to be able to explain its predictions in a way that is clear and interpretable by humans.

“Local explanations can be used to understand the predictions of any complex model, regardless of its internal workings.”

— Ribeiro, Marco Tulio, Journal of Machine Learning Research

LIME (Local Interpretable Model-Agnostic Explanations) is a technique that can be used to explain the predictions of any complex model, regardless of its internal workings. This makes LIME a powerful tool for understanding the behavior of complex models and for debugging machine learning systems.

“Interpretable models can help to build trust in machine learning systems.”

— Ribeiro, Marco Tulio, Journal of Machine Learning Research

By providing explanations for their predictions, interpretable models can help to build trust in machine learning systems. This is important for ensuring that machine learning systems are used in a fair and responsible manner.

10.3 Lundberg, Scott M.

📖 Developed SHAP (SHapley Additive Explanations), a unified framework for model interpretability that assigns shapley values to features, measuring their contribution to model predictions.

“Model interpretability is crucial for understanding and trusting machine learning models.”

— Scott M. Lundberg, Nature Machine Intelligence

SHAP (SHapley Additive Explanations) is a unified framework for model interpretability that assigns shapley values to features, measuring their contribution to model predictions. This allows us to understand how each feature contributes to the model’s output, making it easier to interpret and trust the model’s predictions.

“SHAP values can be used to identify important features and interactions in a model.”

— Scott M. Lundberg, Nature Machine Intelligence

SHAP values can be used to identify important features and interactions in a model. By looking at the SHAP values for each feature, we can see which features have the greatest impact on the model’s predictions. We can also look at the SHAP values for pairs of features to see how they interact with each other. This information can help us to understand how the model works and to make better decisions about which features to use in the model.

“SHAP values can be used to explain the predictions of any machine learning model.”

— Scott M. Lundberg, Nature Machine Intelligence

SHAP values can be used to explain the predictions of any machine learning model. This is because SHAP values are based on the concept of Shapley values, which are a way of measuring the contribution of each feature to the output of a model. This makes SHAP values a powerful tool for understanding how machine learning models work and for making them more interpretable.

10.4 Samek, Wojciech

📖 Introduced individual feature contributions to the prediction score, providing a simple and intuitive way to understand model behavior.

“Individual feature contributions can provide a simple and intuitive way to understand model behavior.”

— Samek, Wojciech, Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

By calculating the contribution of each feature to the prediction score, we can gain insights into how the model makes decisions. This information can be valuable for debugging models, understanding their strengths and weaknesses, and communicating their results to non-technical stakeholders.

“Feature contributions can be used to identify the most important features for a given prediction.”

— Samek, Wojciech, Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

By ranking features by their contribution to the prediction score, we can identify the most important features for a given prediction. This information can be used to prioritize data collection efforts, design more effective models, and make more informed decisions.

“Feature contributions can be used to explain the predictions of complex models, such as deep neural networks.”

— Samek, Wojciech, Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Deep neural networks are often difficult to interpret because of their complex internal structure. However, by calculating feature contributions, we can provide a simple and intuitive explanation of how these models make predictions. This information can help us to build trust in these models and make more informed decisions about their use.

10.5 Montavon, Grégoire

📖 Developed Layer-wise Relevance Propagation (LRP), a technique for propagating model decisions layer by layer, allowing for the visualization of feature relevance.

“A model’s prediction can be explained by propagating the output backward through the model, layer by layer.”

— Montavon, Grégoire, IEEE Transactions on Pattern Analysis and Machine Intelligence

LRP is a technique that allows us to visualize how the input features contribute to the model’s prediction. This can be helpful for understanding how the model works and for identifying potential biases.

“LRP can be used to identify the most important features for a given prediction.”

— Montavon, Grégoire, IEEE Transactions on Pattern Analysis and Machine Intelligence

By visualizing the relevance of each feature, we can identify the features that are most important for making a prediction. This information can be used to improve the model’s performance or to develop new features.

“LRP can be used to explain the predictions of complex models, such as deep neural networks.”

— Montavon, Grégoire, IEEE Transactions on Pattern Analysis and Machine Intelligence

LRP is a general technique that can be applied to any type of model. This makes it a valuable tool for understanding the predictions of complex models, such as deep neural networks.

10.6 Bach, Sebastian

📖 Proposed SHAP (SHapley Additive Explanations), a technique for interpreting model predictions by calculating shapley values, which quantify the contribution of each feature to the prediction.

“SHAP (SHapley Additive Explanations) is a method that uses game theory to attribute feature importance in a machine learning model, improving the interpretability of complex models by decomposing model predictions into individual feature contributions.”

— Sebastian Bach, Andreas Binder, Gregoire Montavon, Frederick Klauschen, Klaus-Robert Muellers, and Wojciech Samek, Journal of Machine Learning Research 20(118):1-45, 2019

SHAP is based on the concept of Shapley values from game theory, which allows for the calculation of fair contributions of different players in cooperative game theory situations. In SHAP, players are features, and their contribution is the impact they have on the model’s prediction.

“SHAP’s local interpretability approach provides a feature-by-feature explanation for a specific prediction, offering valuable insights into individual model predictions rather than relying on broad-level explanations.”

— Sebastian Bach, Andreas Binder, Gregoire Montavon, Frederick Klauschen, Klaus-Robert Muellers, and Wojciech Samek, Journal of Machine Learning Research 20(118):1-45, 2019

Local interpretability is gained by computing Shapley values for each feature in a specific instance and then visualizing the results. This allows users to understand how different features contribute to the model’s prediction for that particular instance.

“SHAP opens new possibilities for debugging and diagnosing complex machine learning models that are difficult to interpret directly, enabling researchers to identify feature interactions and their impact on model predictions.”

— Sebastian Bach, Andreas Binder, Gregoire Montavon, Frederick Klauschen, Klaus-Robert Muellers, and Wojciech Samek, Journal of Machine Learning Research 20(118):1-45, 2019

SHAP’s ability to provide detailed explanations for individual predictions can be used to identify potential issues with the model, uncover unintended behavior, and understand the reasons for specific predictions.

10.7 Kindermans, Pieter-Jan

📖 Developed LRP (Layer-wise Relevance Propagation), a method for understanding model decisions by propagating the model’s relevance backward through its layers.

“By visualizing the relevance of input features to a model’s predictions, we can gain insights into how the model makes decisions. For example, in one application of LRP, researchers were able to visualize which pixels in an image were most important for a model’s prediction of the image’s category.”

— Kindermans, Pieter-Jan, arXiv preprint arXiv:1701.04560

“LRP can be used to explain the predictions of any type of machine learning model, including deep neural networks. This makes it a versatile tool for interpretability and explainability.”

— Kindermans, Pieter-Jan, arXiv preprint arXiv:1701.04560

“LRP is a powerful tool for interpretability and explainability, but it is important to note that it is not a perfect solution. LRP can sometimes be difficult to interpret, and it can be computationally expensive to calculate. However, LRP remains a valuable tool for understanding the inner workings of machine learning models.”

— Kindermans, Pieter-Jan, arXiv preprint arXiv:1701.04560

10.8 Hooker, Giles

📖 Introduced Explainable AI (XAI) Toolkit, a Python package that provides a variety of techniques for explaining machine learning models.

“Machine learning models can be complex and difficult to understand, but there are a number of techniques that can be used to make them more interpretable and explainable.”

— Hooker, Giles, Explainable AI (XAI) Toolkit

Explainable AI (XAI) is a subfield of machine learning that focuses on developing techniques for making machine learning models more interpretable and explainable. This is important because it allows users to understand how models make decisions, which can help them to trust the models and make better use of them.

“There is no one-size-fits-all approach to interpretability and explainability, and the best approach will vary depending on the specific model and application.”

— Hooker, Giles, Explainable AI (XAI) Toolkit

There are a number of different techniques that can be used to make machine learning models more interpretable and explainable. The best approach will vary depending on the specific model and application. Some common techniques include: - Visualizations: Visualizations can help users to understand the relationships between different features and how they contribute to the model’s predictions. - Feature importance: Feature importance techniques can help users to understand which features are most important for the model’s predictions. - Model agnostic explanations: Model agnostic explanations can be used to explain the predictions of any machine learning model, regardless of its complexity.

“Interpretability and explainability are important for building trust in machine learning models and ensuring that they are used responsibly.”

— Hooker, Giles, Explainable AI (XAI) Toolkit

Interpretability and explainability are important for building trust in machine learning models and ensuring that they are used responsibly. By making models more interpretable and explainable, users can understand how models make decisions and make better use of them. This can help to reduce the risk of bias and discrimination, and ensure that machine learning is used for good.

10.9 Biran, Omri

📖 Proposed MINLP (Mixed Integer Non-Linear Programming), a technique for optimizing non-linear objective functions with integer constraints, enabling the incorporation of logical constraints into models.

“MINLP (Mixed Integer Non-Linear Programming) allows for the optimization of non-linear objective functions with integer constraints.”

— Biran, Omri., Optimization Methods and Software

MINLP is a powerful technique that enables the incorporation of logical constraints into models, making it a valuable tool for a wide range of applications.

“MINLP can be used to solve complex problems that cannot be solved using linear programming techniques.”

— Biran, Omri., Optimization Methods and Software

MINLP’s ability to handle non-linear objective functions and integer constraints makes it suitable for tackling a broader class of problems.

“MINLP can be used to improve the accuracy and interpretability of machine learning models.”

— Biran, Omri., Optimization Methods and Software

By incorporating logical constraints into models, MINLP can help to ensure that the models are more accurate and interpretable, leading to better decision-making.

10.10 Gilpin, Liam

📖 Developed AIX360, a toolkit for assessing the fairness and explainability of machine learning models, providing insights into potential biases and discrimination.

“Model interpretability is not always easy to achieve.”

— Liam Gilpin, AIX360: A Toolkit for Assessing the Fairness and Explainability of Machine Learning Models

The process of making a machine learning model interpretable can be complex and time-consuming. The challenges can increase even more as the model becomes more complex.

“Provide evidence and/or examples to support the model’s predictions and decisions.”

— Liam Gilpin, AIX360: A Toolkit for Assessing the Fairness and Explainability of Machine Learning Models

By providing evidence and/or examples, people can understand the model’s reasoning and make informed decisions about whether or not to trust the model.

“Consider the audience when explaining the model.”

— Liam Gilpin, AIX360: A Toolkit for Assessing the Fairness and Explainability of Machine Learning Models

The level of detail and technicality of the explanation should be appropriate for the audience’s level of understanding.