International business machines corporation (20240135239). GENERATING LOCALLY INVARIANT EXPLANATIONS FOR MACHINE LEARNING simplified abstract
Contents
- 1 GENERATING LOCALLY INVARIANT EXPLANATIONS FOR MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING LOCALLY INVARIANT EXPLANATIONS FOR MACHINE LEARNING - 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
GENERATING LOCALLY INVARIANT EXPLANATIONS FOR MACHINE LEARNING
Organization Name
international business machines corporation
Inventor(s)
Amit Dhurandhar of Yorktown Heights NY (US)
Karthikeyan Natesan Ramamurthy of Pleasantville NY (US)
Kartik Ahuja of White Plains NY (US)
GENERATING LOCALLY INVARIANT EXPLANATIONS FOR MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135239 titled 'GENERATING LOCALLY INVARIANT EXPLANATIONS FOR MACHINE LEARNING
Simplified Explanation
The patent application describes techniques for generating explanations for machine learning models. These techniques involve identifying an ML model, an output from the model, and a set of constraints. By generating neighborhoods based on these constraints, predictors can be created for each neighborhood and combined to create a comprehensive predictor. This combined predictor is then used to create explanations related to the ML model and its output.
- Identifying ML model, output, and constraints
- Generating neighborhoods based on constraints
- Creating predictors for each neighborhood
- Combining predictors to create a comprehensive predictor
- Generating explanations using the combined predictor
Potential Applications
The technology could be applied in various fields such as healthcare, finance, and marketing for explaining the decisions made by machine learning models.
Problems Solved
This technology helps in providing transparency and interpretability to machine learning models, which can be crucial for ensuring trust and understanding of the model's decisions.
Benefits
The benefits of this technology include improved trust in machine learning models, better understanding of model decisions, and the ability to identify and address biases in the models.
Potential Commercial Applications
The technology could be used in industries where explainability of machine learning models is essential, such as in healthcare diagnostics, financial risk assessment, and personalized marketing.
Possible Prior Art
One possible prior art could be the use of SHAP (SHapley Additive exPlanations) values for explaining machine learning models, which also aim to provide interpretability to black-box models.
Unanswered Questions
How does this technology compare to existing methods for explaining machine learning models?
This article does not provide a direct comparison with existing methods for explaining machine learning models.
What are the limitations of this technology in terms of scalability and complexity of ML models?
The article does not address the potential limitations of this technology in terms of scalability and complexity of ML models.
Original Abstract Submitted
techniques for generating explanations for machine learning (ml) are disclosed. these techniques include identifying an ml model, an output from the ml model, and a plurality of constraints, and generating a plurality of neighborhoods relating to the ml model based on the plurality of constraints. the techniques further include generating a predictor for each of the plurality of neighborhoods using the ml model and the plurality of constraints, constructing a combined predictor based on combining each of the respective predictors for the plurality of neighborhoods, and creating one or more explanations relating to the ml model and the output from the ml model using the combined predictor.
