Meta-learning Model Training via Causal Transportability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Meta-learning models can become biased when trained on datasets with imbalanced classes, leading to unethical outcomes due to distribution shifts and ethical concerns such as privacy and security issues, as they require direct access to individual datasets and are prone to biases.
Innovation Solution
A method that determines feature dependency and difference information using user inputs and ethical requirements, allowing the construction of structural causal models to calculate trust scores without direct access to individual datasets, thereby training meta-learning models that incorporate ethical considerations and mitigate biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If meta-learning models are trained on datasets with imbalanced classes, then the model can learn from available data, but the model becomes biased and produces incorrect outcomes
Solution Approach 1:
The patent introduces structural causal models (SCMs) as an intermediary layer between datasets and meta-learning models. SCMs capture causal relationships and allow reasoning about data generation processes without direct access to raw data, enabling fairness assessment and trust score calculation that mediate between dataset characteristics and model training
Solution Approach 2:
The patent replaces direct data-driven training mechanisms with a causal inference-based system. Instead of directly training models on raw datasets, the system uses SCMs to model causal structures and computes trust scores based on ethical coefficients derived from causal transportability, substituting mechanical data processing with causal reasoning
2Reliability
If direct access to individual datasets is required for training, then the model can be trained on actual data, but privacy and security concerns arise
Solution Approach 1:
The patent extracts essential information about datasets into abstract representations called structural causal models. These SCMs capture causal relationships and feature dependencies without containing actual data, allowing training and evaluation while removing sensitive information, thus extracting only the necessary structural information for modeling
Solution Approach 2:
The patent creates copies of dataset characteristics through SCMs that replicate causal structures and relationships without replicating actual data. These causal models serve as data-free representations that enable training while maintaining privacy, as they contain only structural information rather than raw data
3Adaptability or versatility
If datasets are used to train models, then prediction tasks can be performed, but distribution shifts cause biased outcomes across different domains
Solution Approach 1:
The patent develops a universal framework using structural causal models that can represent causal relationships across different domains and datasets. This universal SCM representation allows the same causal modeling approach to be applied to multiple domains (e.g., healthcare, finance, education) while maintaining accuracy by capturing domain-specific causal structures
Solution Approach 2:
The patent changes the representation parameters from raw data to causal structural parameters. By modeling datasets through SCMs with parameters representing causal relationships rather than actual data values, the system can adapt to different domains by adjusting causal parameters while maintaining consistent reasoning, thereby reducing distribution shift issues
Data Source
AI summary
In an embodiment, multiple datasets related to multiple application domains are received. Further, feature dependency information associated with a first dataset is determined, based on a first user input. Also, feature difference information associated with the first dataset and a second dataset is determined, based on a second user input and a set of ethical requirements. A set of structural causal models (SCMs) associated with the first dataset are determined based on the feature dependency information and the feature difference information. A set of ethical coefficients associated with the set of ethical requirements are determined based on an application of a causal transportability model on the set of SCMs. A trust score associated with the first dataset is determined based on the set of ethical coefficients. The trust score is used to train a meta-learning model associated with the multiple application domains.