AI Classifier Audit Trigger for Inventory Accuracy
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Solution Overview
Problem
Current automated inventory systems for Consumer-Packaged Goods (CPGs) require significant human review and continuous dataset refinement due to the high volume of similar products and frequent packaging changes, making it impractical to review every inference and necessitating statistical audits, which increases workload and reduces accuracy.
Innovation Solution
Implementing a computer-assisted method that uses an audit trigger and benefit scoring mechanism to selectively submit inferences with low confidence scores or missing classifications to human auditors for correction, allowing the AI system to learn from feedback and improve its accuracy over time, thereby reducing human workload and improving classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If statistical audit techniques are used to reduce workload, then human review efficiency improves, but classification accuracy deteriorates
Solution Approach 1:
The system implements a feedback mechanism where human audit corrections are fed back into the training dataset, enabling the model to continuously learn from its mistakes and improve accuracy over time while maintaining reduced human review workload
Solution Approach 2:
The audit threshold is made dynamic rather than static, allowing the system to adjust the proportion of inferences sent for human review based on model performance, data characteristics, and confidence scores, optimizing the balance between accuracy and efficiency
2Measurement precision
If every inference is reviewed by humans, then classification accuracy improves, but workload increases significantly
Solution Approach 1:
Instead of reviewing every inference, the system applies partial action by selectively auditing only those inferences that fall below a confidence threshold or exhibit uncertain characteristics, achieving sufficient accuracy without the excessive workload of universal review
Solution Approach 2:
The AI model performs self-correction through continuous learning from human-audited examples, reducing the need for constant human intervention and enabling the system to handle its own performance optimization autonomously
3Measurement precision
If the AI system continuously learns from human feedback, then classification accuracy improves, but training data requirements increase
Solution Approach 1:
The system applies local quality by focusing training efforts on specific uncertain cases identified by the audit trigger rather than uniformly processing all data, concentrating learning resources on the most beneficial examples that address specific classification weaknesses
Data Source
AI summary
A method is provided for training at least one classifier model used by an artificial intelligence (AI) system to recognize each of a set of objects and to assign each of the set of objects to a class. The method includes training the at least one classifier model on a training dataset, thereby producing at least one trained classifier model; using the at least one trained classifier model to detect and classify each member of a set of objects, thereby generating a set of inferences, wherein each inference includes (a) a cropped image of a classified object, (b) the classified object's inferred class, and (c) a confidence score associated with the inferred classification; examining the set of inferences with a machine implemented audit trigger, wherein the audit trigger identifies a subset of the set of inferences whose members have (i) a confidence score that falls below a predetermined threshold value, or (ii) a missing classification; and if the identified subset has at least one member, subjecting the identified subset to a human audit, thereby yielding a corrected set of observations, wherein, for each member of the corrected set of observations, the inferred class of the corresponding member of the set of inferences is replaced with a corrected class. The corrected set of observations is then added to a training dataset and used to improve the future accuracy of the classifier model.