Learning to Assign Framework for Fair Instance Allocation
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Solution Overview
Problem
Current human-AI collaboration methods, such as Learning to Defer (L2D), face limitations in handling human capacity constraints and selective labels, leading to suboptimal decision-making and fairness issues in high-stakes applications, where ML models often exhibit bias against underprivileged groups.
Innovation Solution
The proposed method, Learning to Assign (L2A), assigns training instances to either a constraint-limited or constraint-unlimited classifier by minimizing a composite loss function that accounts for both fairness and capacity constraints, using a machine-learning framework that estimates error probabilities and optimizes assignments across deployment instances while respecting capacity limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Learning to Defer (L2D) is used to assign instances to human or AI classifiers, then decision-making accuracy can be optimized, but human capacity constraints are violated when concurrent predictions from every human are required for every training instance
Solution Approach 1:
The patent applies partial action by requiring human predictions only for a subset of training instances rather than all instances. The system selectively assigns instances to humans based on uncertainty estimates and fairness constraints, allowing the AI classifier to handle the majority of instances independently. This resolves the contradiction by maintaining high decision-making accuracy through targeted human review while preserving human capacity for productivity.
2Speed
If ML models are used to process large volumes of information quickly, then processing speed and scalability are improved, but fairness issues arise when models exhibit bias against underprivileged groups
Solution Approach 1:
The patent implements feedback mechanisms where human predictions serve as ground truth to correct and retrain the AI classifier. The system continuously monitors fairness metrics and uses human-labeled instances to refine the model's decision boundaries. This feedback loop enables the system to maintain high processing speed while progressively reducing algorithmic bias through iterative improvement based on human feedback.
Solution Approach 2:
The patent introduces human classifiers as intermediaries between the AI model and final decisions for uncertain or potentially biased instances. The assignment mechanism acts as a mediator that routes suspicious cases to human review, allowing the system to leverage both the speed of AI processing and the fairness awareness of human judgment. This intermediary approach resolves the contradiction by filtering bias-prone cases for human review while maintaining efficient AI processing for clear cases.
3Reliability
If human classifiers are used to ensure fairness and adaptability in decision-making, then fairness and causal reasoning improve, but processing speed and scalability deteriorate due to human limitations
Solution Approach 1:
The patent segments the classification task into two distinct pathways: an AI-driven path for high-speed processing of clear-cut cases, and a human-in-the-loop path for fairness-critical uncertain cases. The uncertainty estimation mechanism divides instances between these pathways based on their risk profile. This segmentation resolves the contradiction by assigning speed-critical tasks to AI and fairness-critical tasks to humans, optimizing both processing speed and reliability simultaneously.
Data Source
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AI summary
The present document discloses a computer-implemented method for assigning one or more training instances for classification to one or more constraint-limited classifiers or a constraint-unlimited machine-learning classifier, wherein said classified training instances are for training a target machine-learning model. It is further disclosed a method for initializing a method for assigning one or more training instances for classification to one or more constraint-limited classifiers or a constraint-unlimited machine-learning classifier; and a machine-learning training method for assignment of one or more training instances for classification to one or more constraint-limited classifiers or a constraint-unlimited machine-learning classifier.