Human-in-the-Loop Boosting for Interpretable Predictive Models
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Solution Overview
Problem
Machine learning models, particularly in health sciences, lack interpretability, leading to a lack of trust among end-users such as physicians due to their 'black box' nature, making it difficult for them to understand how the models work and make informed decisions.
Innovation Solution
A method for training a predictive model that incorporates human-understandable predicates and uses a boosting algorithm with human input to iteratively select and deselect predicates, ensuring the model is interpretable and trustworthy by gradually building a boosting model using human-understandable predicates and allowing expert input during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for machine learning models, then prediction accuracy is improved, but model interpretability deteriorates
Solution Approach 1:
The patent segments the model into two distinct components: a complex deep neural network for accurate predictions and a separate interpretable model (decision tree or rule-based system) for explanations. This segmentation allows each component to specialize - the neural network handles accuracy while the interpretable model handles transparency, resolving the contradiction between accuracy and interpretability.
Solution Approach 2:
The patent introduces an intermediary interpretable model that mediates between the black-box neural network and human users. This intermediary translates the neural network's predictions into human-understandable forms (decision trees, rules, or feature attributions), allowing users to trust and understand the system without sacrificing the neural network's predictive accuracy.
2Reliability
If complex models are used to handle diverse medical data, then predictive performance is improved, but trustworthiness deteriorates
Solution Approach 1:
The system segments functionality into a performance-optimized neural network and a trust-optimized interpretable model. The neural network processes complex medical data for high predictive performance, while the separate interpretable model provides transparent reasoning that builds trust, allowing both reliability and trustworthiness to coexist.
Solution Approach 2:
The patent implements feedback mechanisms where the interpretable model's explanations are evaluated by users, and this feedback is used to refine both the interpretable model and potentially the neural network. This continuous improvement loop enhances both predictive performance and trustworthiness over time.
3Measurement precision
If black box models are used for predictions, then accuracy is improved, but understandability deteriorates
Solution Approach 1:
The patent divides the system into an accuracy-providing black-box neural network and an understandability-providing interpretable model. Users receive both accurate predictions from the neural network and understandable explanations from the interpretable model, eliminating the need to choose between accuracy and understandability.
Solution Approach 2:
The interpretable model creates a simplified copy or approximation of the neural network's decision-making process. This copy maintains the essential predictive logic in a human-readable form, allowing users to understand the reasoning without sacrificing the original neural network's accuracy.
Data Source
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AI summary
A method is described for training a predictive model which increases the interpretability and trustworthiness of the model for end-users. The model is trained from data having multitude of features. Each feature is associated with a real value and a time component. Many predicates (atomic elements for training the model) are defined as binary functions operating on the features, and typically time sequences of the features or logical combinations thereof. The predicates can be limited to those functions which have human understandability or encode expert knowledge relative to a predication task of the model. We iteratively train a boosting model with input from an operator or human-in-the-loop. The human-in-the-loop is provided with tools to inspect the model as it is iteratively built and remove one or more of the predicates in the model, e.g. if it does not have indicia of trustworthiness, is not causally related to a prediction of the model, or is not understandable. We repeat the iterative process several times ultimately generate a final boosting model. The final model is then evaluated, e.g., for accuracy, complexity, trustworthiness and post-hoc explainability.