ML Decision Traceback Rationales for Explainable Outcomes
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Solution Overview
Problem
Existing machine-learning (ML) models lack explainability, particularly with neural networks and support vector machines, making it difficult to understand decision paths leading to events with poor or successful outcomes, which hinders effective human-in-the-loop decision making in critical systems.
Innovation Solution
Integrate traceback capabilities with ML techniques to generate and query decision rationales, allowing for retrospective interpretation of decision sequences, including direct, counterfactual, and contrastive queries to determine the logic behind outcomes and identify human or machine contributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ML models (neural networks, support vector machines) are used to automate decision making, then productivity and automation extent are improved, but explainability and interpretability deteriorate
Solution Approach 1:
The patent introduces decision rationales as an intermediary layer between the ML model and the decision output. This rationale layer captures the decision-making logic and pathways, allowing external agents to understand and interpret model decisions without modifying the underlying stochastic ML model. The rationale acts as a mediator that translates complex model behavior into interpretable decision sequences.
Solution Approach 2:
The system performs preliminary action by generating and storing decision rationales alongside model outputs. Instead of attempting to explain decisions after they are made, the rationale is created in advance as part of the decision-making process, enabling retrospective analysis and explanation of decision paths before queries are submitted.
2Manufacturing precision
If stochastic optimization of weights and model parameters is used, then manufacturing precision and model performance are improved, but explainability deteriorates
Solution Approach 1:
The decision rationale serves as an intermediary that bridges the gap between stochastic model parameters and interpretable decision paths. It captures the effective decision logic without requiring direct observation or interpretation of the stochastic weight optimizations, thereby maintaining both model performance and explainability.
Solution Approach 2:
The system creates a copy of the decision logic in the form of decision rationales. Rather than attempting to directly observe or measure the complex stochastic optimization processes, the rationale captures and replicates the essential decision-making pathways in a form that is easy to detect, measure, and interpret.
3Device complexity
If black box ML models are deployed, then device complexity is reduced, but the ability to determine decision paths for poor or successful outcomes deteriorates
Solution Approach 1:
The patent segments the decision-making system into two distinct components: the simple black box ML model and the separate decision rationale layer. This segmentation allows the model to remain structurally simple while the rationale layer independently provides the necessary decision path information, resolving the contradiction between simplicity and explainability.
Solution Approach 2:
The decision rationale serves multiple functions simultaneously: it explains successful outcomes, analyzes poor outcomes, enables retrospective interpretation, and provides decision paths for external agents. This multi-functionality allows a single addition (the rationale layer) to address multiple information needs without increasing model complexity.
Data Source
AI summary
Techniques for providing decision rationales for machine-learning guided processes are described herein. In some embodiments, the techniques described herein include processing queries for an explanation of an outcome of a set of one or more decisions guided by one or more machine-learning processes with supervision by at least one human operator. Responsive to receiving the query, a system determines, based on a set of one or more rationale data structures, whether the outcome was caused by human operator error or the one or more machine-learning processes. The system then generates a query response indicating whether the outcome was caused by the human operator error or the one or more machine-learning processes.