Linking Actions to ML Prediction Explanations
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Solution Overview
Problem
Current machine learning prediction models lack the ability to effectively link actions to mitigate negative factors influencing their outcomes, leading to suboptimal performance and a lack of transparency in decision-making processes.
Innovation Solution
A method is provided to link recommended actions to features influencing machine learning prediction models, allowing for the identification and mitigation of negative factors by applying specific actions to improve prediction accuracy, with a focus on cost-effectiveness and user-driven service integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning prediction models are used to make predictions, then prediction capability is improved, but transparency and ability to link actions to mitigate negative factors deteriorate
Solution Approach 1:
The system implements feedback by analyzing prediction outcomes and identifying negative factors, then recommending specific actions to mitigate those factors. This creates a closed-loop system where predictions inform actions, and actions feed back into improving future predictions, maintaining both accuracy and transparency throughout the process.
Solution Approach 2:
The system introduces an intermediary layer between the prediction model and the decision-making process. This intermediary analyzes the model's predictions, identifies negative factors, and translates them into actionable recommendations, making the black-box prediction process transparent and actionable without sacrificing prediction accuracy.
2Reliability
If recommended actions are linked to features influencing prediction models, then ability to mitigate negative factors is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of improving prediction outcomes into distinct components: identifying negative factors, recommending specific actions, and implementing those actions. This segmentation makes the system more manageable and less complex while maintaining the ability to effectively mitigate negative factors through targeted interventions.
Solution Approach 2:
The system enables self-service by automatically analyzing predictions, identifying negative factors, and generating actionable recommendations without requiring extensive manual intervention. This automation reduces operational complexity while improving the system's ability to reliably mitigate negative factors through consistent, data-driven actions.
3Measurement precision
If targeted actions are applied to improve prediction accuracy, then prediction performance is improved, but resource allocation complexity increases
Solution Approach 1:
The system changes parameters by identifying specific features and their negative factors, then applying targeted actions that modify those parameters. This approach improves prediction accuracy by focusing resources on the most impactful parameters rather than distributing resources uniformly, thereby reducing resource allocation complexity through prioritization.
Solution Approach 2:
The system applies partial action by focusing resources on specific negative factors rather than attempting to address all possible issues. By concentrating efforts on the most critical factors that negatively impact predictions, the system improves accuracy efficiently without the complexity of comprehensive resource allocation across all potential areas.
Data Source
AI summary
Embodiments for recommending actions to improve machine learning predictions by a processor. One or more recommended actions may be linked to one or more features that influence a predicted outcome of a prediction model of a machine learning operation. One or more features having one or more negative factors that negatively impact the predicted outcome of the prediction model may be determined and selected. One or more of the linked recommended actions may be applied to one or more of the features to mitigate a negative impact upon the predicted outcome of the prediction model.