Adaptive AI Decision Engine Risk Confidence Feedback
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Solution Overview
Problem
Traditional decision engines lack visibility into their decision-making processes, making it difficult to improve and increase confidence in decision results, as they do not provide reasons for their outputs.
Innovation Solution
An AI engine determines a risk level and confidence level for decisions, and employs groups of reviewers to provide feedback that refines the AI engine's model, incorporating risk and confidence assessments and leveraging natural language processing to enhance decision-making accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional decision engines automatically make decisions using AI routines, then decision speed and productivity are improved, but visibility into the decision-making process deteriorates making it difficult to improve and increase confidence in decision results
Solution Approach 1:
The system implements feedback by having reviewers evaluate AI-generated decisions and provide corrections or confirmations. This feedback loop allows the system to learn from human expertise while maintaining automated decision speed, thereby improving both productivity and decision-making visibility simultaneously
Solution Approach 2:
The system introduces reviewers as intermediaries between the AI decision engine and the final decision outcome. These reviewers provide the missing visibility and reasoning that traditional automated systems lack, while the AI maintains its speed advantage, resolving the contradiction between automation and transparency
2Measurement precision
If reviewer feedback is incorporated to improve model accuracy, then decision accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system applies partial action by having reviewers evaluate only a subset of decisions (those below confidence threshold or flagged for review) rather than all decisions. This selective approach improves accuracy for critical decisions while limiting the increase in system complexity and processing time
Solution Approach 2:
The system dynamically adjusts the confidence threshold parameter to control the balance between automated and human-reviewed decisions. By changing this parameter, the organization can optimize the trade-off between decision accuracy and system complexity based on specific needs
Data Source
AI summary
Techniques are described for adaptive and augmented decision making by an artificial intelligence (AI) engine, such as an engine that employs machine learning techniques. A decision-making process may be executed to make a decision regarding operations of the organization, and the AI engine may be employed to analyze the various aspects of a decision and determine a risk level associated with the decision. The risk level may be a combination of the probability of a negative outcome and a magnitude of loss that may occur due to a negative outcome. The automated process may also determine a confidence level that indicates a degree of confidence in the determined risk level. Risk and confidence may be independent values. Implementations may enable risk mitigation by providing a risk estimate with higher confidence than traditional methods.