ML Project Bias Scoring Using Clustering and Corrective Feedback
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Solution Overview
Problem
Existing machine learning systems introduce unintentional bias that can go unrecognized and uncorrected, posing risks to businesses and organizations, with current methods lacking the ability to predict and mitigate potential bias in projects at the outset.
Innovation Solution
A system and method using machine learning to cluster projects based on potential bias characteristics, extract features and metadata, and provide bias risk scores and recommendations for mitigation, utilizing a bias ontology and clustering framework to identify and reduce bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning processes are used to analyze project data, then productivity and automation are improved, but unintentional bias is introduced into the data and models
Solution Approach 1:
The system implements feedback by using machine learning models to detect bias in project data, then feeding this bias information back to adjust and improve subsequent project analyses. The bias detection results are stored and used to refine future predictions, creating a continuous improvement loop that reduces bias while maintaining automated analysis capabilities
Solution Approach 2:
The patent introduces an intermediary bias detection layer between the machine learning analysis process and the final project outcomes. This intermediary component analyzes project data for bias characteristics without being part of the core business logic, allowing bias to be identified and corrected separately from the main productivity-driven analysis processes
2Reliability
If bias detection and mitigation systems are implemented, then reliability and fairness are improved, but device complexity and computational resources increase
Solution Approach 1:
The system segments bias detection into separate, modular components: feature extraction modules that identify potential bias indicators, clustering modules that group similar projects, and scoring modules that quantify bias risk. This segmentation allows each component to be optimized independently and reduces overall system complexity by creating manageable, reusable modules
Solution Approach 2:
The patent uses copying by creating simplified representations of complex project data through feature extraction. Instead of analyzing entire project datasets, the system extracts key features and metadata that capture essential bias characteristics, creating smaller copies that are computationally efficient to process while maintaining detection accuracy
3Measurement precision
If clustering and feature extraction are performed on all project data, then measurement precision of bias characteristics is improved, but loss of time and computational resources increase
Solution Approach 1:
The system applies partial action by performing feature extraction and clustering on only the most relevant features and a subset of projects rather than analyzing all data comprehensively. The bias scoring model focuses on extracting and analyzing key characteristics that have the highest impact on bias detection, achieving sufficient precision without the time cost of complete data processing
Solution Approach 2:
The patent implements preliminary action by pre-processing project data to extract features and create initial clusterings before bias analysis is needed. This preliminary structuring of data allows faster, more efficient bias detection when projects are evaluated, as the heavy lifting of data organization is already complete
Data Source
AI summary
Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.