Feature Attribution Capture in ML Pipelines
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Solution Overview
Problem
Machine learning models can produce biased outcomes due to various factors, making it challenging to detect and mitigate discriminatory impacts, especially in applications where ethical, policy, and legal compliance is necessary, as existing methods lack comprehensive tools for staged bias measurements and feature attribution across different stages of the pipeline.
Innovation Solution
Implementing a machine learning pipeline with staged bias measurements and feature attribution capture, which includes pre-training and post-training bias metrics, as well as global and local explainability techniques, such as Shapley values, to provide transparent and interpretable decision-making processes, ensuring fairness and accountability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are deployed to improve accuracy and productivity, then decision-making benefits are achieved, but biased outcomes and discriminatory impacts occur that are difficult to detect and mitigate
Solution Approach 1:
The patent segments the machine learning pipeline into multiple stages (data collection, preprocessing, training, evaluation, deployment) and applies bias measurements at each stage. This segmentation allows for targeted detection and mitigation of biases at specific points in the pipeline, rather than treating bias as a monolithic problem.
Solution Approach 2:
The patent introduces feature attribution techniques as intermediary tools that explain the relationship between input features and model predictions. These intermediaries (explanation mechanisms) bridge the gap between the opaque model decisions and human understanding, enabling detection of biased outcomes through interpretability.
2Difficulty of detecting and measuring
If comprehensive bias measurement tools are implemented across all pipeline stages, then detection capability improves, but system complexity increases
Solution Approach 1:
By dividing bias measurement into stage-specific tools (preprocessing bias detection, training bias measurement, deployment bias monitoring), the system reduces the complexity of any single measurement tool while maintaining comprehensive detection capability across the entire pipeline.
Solution Approach 2:
The patent applies different measurement techniques appropriate to each pipeline stage's specific characteristics and requirements. For example, using feature attribution methods at the model level while using statistical analysis at the data level, rather than applying a single complex solution uniformly across all stages.
3Loss of information
If feature attribution and explainability techniques are applied, then model interpretability improves, but computational overhead increases
Solution Approach 1:
The patent implements feature attribution selectively at key pipeline stages rather than continuously throughout. By applying explainability techniques partially (at training completion and deployment checkpoints) rather than excessively (at every processing step), the system achieves necessary transparency while controlling computational overhead.
Data Source
AI summary
Feature attribution may be captured as part of a machine learning pipeline. A training job may include a request to determine feature attribution as part of a machine learning pipeline that trains a machine learning model from a training data set. A reference data set for determining the feature attribution of the machine learning model may be identified. The feature attribution may be determined based on the reference data set. The feature attribution of the trained machine learning model may be stored.