Multi-dimensional Risk Optimization for Predictive Models
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Solution Overview
Problem
Evaluating machine learning models across multiple evaluation criteria, such as accuracy, fairness, and privacy, often involves making trade-offs between conflicting objectives, which can be complex and require input from stakeholders.
Innovation Solution
A computer-implemented method and system that receives candidate trained machine learning models and evaluation dimensions, generates risk scores, determines correlations between dimensions, and performs an optimization calculation to identify a subset of models that optimizes an overall risk measure across all dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are evaluated on multiple risk-related metrics (fairness, bias, privacy compliance), then model reliability and societal benefit are improved, but the complexity of the evaluation process increases
Solution Approach 1:
The patent segments the complex multi-dimensional risk evaluation into distinct evaluation dimensions (fairness, bias, privacy compliance, etc.), each assessed independently through specific metrics. This segmentation allows the system to manage complexity by breaking down the overall evaluation into manageable components while maintaining comprehensive coverage of all risk aspects.
Solution Approach 2:
The patent introduces an intermediary optimization system that mediates between conflicting evaluation dimensions. This intermediary layer processes the multi-dimensional risk scores and applies optimization algorithms to balance competing objectives, thereby managing evaluation complexity while ensuring all dimensions are considered in the final model selection.
2Adaptability or versatility
If stakeholders are involved to determine the relative importance of each metric, then the adaptability of the evaluation to specific needs is improved, but the time and resources required for evaluation increase
Solution Approach 1:
The patent implements dynamic weighting mechanisms that allow evaluation criteria to be adjusted based on stakeholder input and specific application contexts. The system can dynamically modify the importance assigned to different evaluation dimensions without requiring complete re-evaluation, thereby maintaining adaptability while reducing time loss through iterative optimization.
Solution Approach 2:
The patent employs preliminary action by pre-defining evaluation dimensions and metrics frameworks before actual model evaluation. Stakeholders can pre-specify their preferences and constraints, allowing the optimization system to efficiently process evaluations without requiring extensive real-time deliberation, thus reducing evaluation time while preserving adaptability.
3Reliability
If more personal information is collected to increase fairness in hiring decisions, then fairness is improved, but privacy compliance may be compromised
Solution Approach 1:
The patent applies parameter changes by transforming the evaluation from a binary fairness assessment to a multi-dimensional risk scoring system. This allows the system to quantify and balance fairness improvements against privacy risks through optimized parameter weighting, enabling decisions that achieve acceptable fairness levels without exceeding privacy compliance thresholds.
Solution Approach 2:
The patent converts the potential harm of privacy violations into a beneficial optimization constraint. By framing privacy compliance as an evaluation dimension with associated risk scores, the system transforms what could be a harmful trade-off into a structured optimization problem where privacy constraints guide the selection of fairness-improving measures that remain within acceptable boundaries.
Data Source
AI summary
A computer-implemented method comprising: receiving a set of candidate trained machine learning models and a set of evaluation dimensions; generating risk scores for each of the candidate trained machine learning models over each of the evaluation dimensions; determining correlations between the evaluation dimensions based, at least in part, on the generated risk scores; and performing an optimization calculation to identify a subset of the set of candidate trained machine learning models, wherein each of the candidate trained machine learning models in the subset optimizes an overall risk measure over all of the evaluation dimensions, wherein the optimization calculation is based, at least in part, on the determined correlations.