Bias Eliminated Performance Determination Using Biological Feedback
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Solution Overview
Problem
Machine learning models are prone to incorporating human biases, which can lead to inaccurate performance determinations.
Innovation Solution
An apparatus and method that utilize biological feedback from users to compare with performance parameters, generating performance determinations through a machine learning model and classifying them to bias categories to eliminate bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used for performance determination, then productivity is improved, but measurement precision deteriorates due to incorporation of human biases
Solution Approach 1:
The patent introduces biological feedback data as an intermediary element that mediates between the machine learning model and the performance determination process. This biological feedback serves as a mediator to counterbalance human biases incorporated in the ML model, thereby improving measurement precision while maintaining productivity
Solution Approach 2:
The patent changes the parameters used in performance determination by incorporating biological feedback parameters (physiological, behavioral, cognitive data) alongside traditional performance metrics. This parameter expansion allows the system to account for individual differences and reduce bias, improving measurement accuracy without sacrificing efficiency
2Device complexity
If machine learning models incorporate human biases, then device complexity is reduced, but reliability deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where biological feedback data is continuously collected and used to adjust and refine performance determinations. This feedback loop enhances reliability by providing ongoing validation and correction of ML model outputs, making the system more trustworthy without increasing structural complexity
3Measurement precision
If bias classification is added to the system, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent designs the machine learning model to serve multiple functions: it simultaneously performs performance determination and bias detection/classification. This multi-functionality allows the system to improve measurement precision through bias classification without proportionally increasing device complexity, as the same computational infrastructure serves dual purposes
Data Source
AI summary
In an aspect, an apparatus for bias eliminated performance determination is presented. An Apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory includes instructions configuring at least a processor to receive, through a sensing device, biological feedback of a user. At least a processor is configured to compare biological feedback to a performance parameter of a task. At least a processor is configured to generate, as a function of a comparison, a performance determination through a performance determination machine learning model. At least a processor is configured to classify a performance determination to a bias category as a function of a bias classifier. At least a processor is configured to train a performance determination machine learning model with biological feedback and a bias classification of a performance determination.