Capital one services, llc (20240112052). SYSTEMS AND METHODS FOR COUNTERFACTUALS IN MACHINE LEARNING APPLICATIONS simplified abstract
Contents
- 1 SYSTEMS AND METHODS FOR COUNTERFACTUALS IN MACHINE LEARNING APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR COUNTERFACTUALS IN MACHINE LEARNING APPLICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SYSTEMS AND METHODS FOR COUNTERFACTUALS IN MACHINE LEARNING APPLICATIONS
Organization Name
Inventor(s)
Brian Barr of Schenectady NY (US)
Samuel Sharpe of Cambridge MA (US)
Christopher Bayan Bruss of Washington DC (US)
SYSTEMS AND METHODS FOR COUNTERFACTUALS IN MACHINE LEARNING APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112052 titled 'SYSTEMS AND METHODS FOR COUNTERFACTUALS IN MACHINE LEARNING APPLICATIONS
Simplified Explanation
The abstract describes a patent application for a computing system that uses machine learning and counterfactual samples to generate explanations for model predictions, train models to be consistent with previous outputs, and create counterfactual samples based on user preferences.
- The computing system aggregates multiple counterfactual samples to generate machine learning explanations for sub-populations.
- It uses machine learning and counterfactual samples to determine text for explanations.
- The system trains models to accept action requests and generate consistent output.
- It generates counterfactual samples based on user preferences by adjusting features indicated by the preferences.
Potential Applications
This technology could be applied in various fields such as finance, healthcare, and marketing for generating personalized explanations for model predictions, improving model consistency, and creating tailored recommendations based on user preferences.
Problems Solved
This technology addresses the challenges of explaining machine learning predictions, ensuring model consistency, and generating personalized recommendations by utilizing counterfactual samples and user preferences.
Benefits
The benefits of this technology include improved transparency in machine learning models, enhanced user experience through personalized recommendations, and increased trust in model outputs due to consistency.
Potential Commercial Applications
Potential commercial applications of this technology include personalized recommendation systems, predictive analytics platforms, and automated decision-making tools in industries such as e-commerce, healthcare, and finance.
Possible Prior Art
One possible prior art for this technology could be the use of counterfactual explanations in machine learning models to improve transparency and interpretability of predictions.
Unanswered Questions
How does the computing system handle conflicting user preferences when generating counterfactual samples?
The system may prioritize certain preferences over others based on predefined rules or weights assigned to each preference.
What measures are in place to ensure the generated explanations are accurate and reliable?
The system may include validation processes, such as cross-validation techniques or human review, to verify the accuracy of the generated explanations.
Original Abstract Submitted
in some aspects, a computing system may aggregating multiple counterfactual samples so that machine learning explanations can be generated for sub-populations. in addition, methods and systems described herein use machine learning and counterfactual samples to determine text to use in an explanation for a model's prediction. a computing system may also train machine learning models to not only determine whether a request to perform an action should be accepted, but also to generate output that is consistent with output generated by previous machine learning models. further, a computing system may generate counterfactual samples based on user preferences. a computing system may obtain preferences and then apply a penalty or adjustment parameter such that when a counterfactual sample is created, the computing system is forced to change one or more features indicated by the preferences to create the counterfactual sample.
