International business machines corporation (20240160965). DATA MINIMIZATION USING LOCAL MODEL EXPLAINABILITY simplified abstract
Contents
- 1 DATA MINIMIZATION USING LOCAL MODEL EXPLAINABILITY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DATA MINIMIZATION USING LOCAL MODEL EXPLAINABILITY - 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
DATA MINIMIZATION USING LOCAL MODEL EXPLAINABILITY
Organization Name
international business machines corporation
Inventor(s)
Abigail Goldsteen of Haifa (IL)
DATA MINIMIZATION USING LOCAL MODEL EXPLAINABILITY - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160965 titled 'DATA MINIMIZATION USING LOCAL MODEL EXPLAINABILITY
Simplified Explanation
The embodiment described in the abstract involves generating feature explainability data for a predictive model based on the significance of a feature for the model's output. This data is then used to construct a generalization group and generate generalized domain data.
- Feature explainability data is generated for a feature in a set of features of a sample represented by input data for a predictive model.
- Feature value data is extracted from the input data to represent the feature value of the sample.
- A generalization group is constructed by detecting a predetermined condition between the feature value and explainability value.
- Generalized domain data is generated to indicate a generalized domain that includes a generalized feature value corresponding to multiple feature values in the generalization group.
Potential Applications
This technology could be applied in various fields such as:
- Predictive analytics
- Machine learning
- Data science
Problems Solved
This technology helps in:
- Understanding the significance of features in predictive models
- Improving model interpretability
- Enhancing decision-making processes
Benefits
The benefits of this technology include:
- Increased transparency in predictive models
- Better understanding of model outputs
- Improved trust in AI systems
Potential Commercial Applications
Potential commercial applications of this technology could include:
- Financial forecasting
- Healthcare diagnostics
- Risk assessment in insurance
Possible Prior Art
One possible prior art related to this technology is the use of feature importance techniques in machine learning models to understand the impact of different features on model predictions.
What are the specific conditions that determine the construction of a generalization group?
The specific conditions that determine the construction of a generalization group are not explicitly mentioned in the abstract. Further details may be provided in the full patent application.
How does the technology handle cases where multiple features are significant for the model output?
The abstract does not address how the technology handles cases where multiple features are significant for the model output. This aspect may be elaborated on in the detailed description of the patent application.
Original Abstract Submitted
an embodiment includes generating feature explainability data associated with a feature in a set of features of a sample represented by input data for a predictive model, where the explainability value is based at least in part on a significance of the feature for an output of the predictive model for the sample. the embodiment extracts feature value data from the input data that is representative of a feature value of the feature for the sample. the embodiment constructs a generalization group comprising the feature of the sample by detecting that the feature value and the explainability value satisfy a predetermined condition. the embodiment generates generalized domain data indicative of a generalized domain that comprises a generalized feature value that corresponds to a plurality of feature values in a domain of the generalization group such that the generalized feature is a generalization of the feature of the sample.
