Reinforcement Learning Agent for Counterfactual Explanations
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Solution Overview
Problem
Current methods for generating counterfactual explanations for black-box machine learning models are computationally expensive and require access to the model's composition or gradients, limiting their applicability and efficiency.
Innovation Solution
The approach recasts the calculation of counterfactual explanations as a Markov Decision Process and employs reinforcement learning to train an agent that can generate explanations without requiring knowledge of the model's structure or gradients, using output data, training data, and actionable features to create a state space, action space, transition function, and reward function within a Markov Decision Process framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gradient descent or optimization methods are used to generate counterfactual explanations, then explanation accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent pre-trains a reinforcement learning agent on the MDP formulation of counterfactual explanation generation. This preliminary training phase allows the agent to learn optimal explanation generation strategies in advance, so that during actual use, explanations can be generated quickly without requiring expensive gradient descent or optimization methods for each individual explanation request.
2Ease of operation
If black-box model access is used, then model structure knowledge requirement is reduced, but explanation generation speed decreases
Solution Approach 1:
The patent replaces traditional gradient-based optimization methods (mechanical system) with a reinforcement learning agent that operates in a Markov Decision Process framework. This substitution allows the system to work with black-box models without requiring gradient access or model structure knowledge, while maintaining efficient explanation generation through the pre-trained agent's learned policies.
3Measurement precision
If model structure knowledge is required, then explanation precision is improved, but system complexity increases
Solution Approach 1:
The patent introduces a reinforcement learning agent operating in an MDP framework as an intermediary between the user and the black-box model. This intermediary learns to generate accurate counterfactual explanations without requiring direct access to model structure or gradients, thereby maintaining explanation precision while avoiding the complexity of implementing and managing gradient-based optimization systems.
Data Source
AI summary
In an embodiment, the systems and methods discussed herein are related to generating, via a processor, a Markov Distribution Problem (MDP), the MDP including a state space, an action space, a transition function, a reward function, and a discount factor. A reinforcement learning (RL) model is applied, via the processor, to the MDP to generate a RL agent. An input data associated with a first user is received at the RL agent. At least one counterfactual explanation (CFE) is generated via the processor and by the RL agent and based on the input data. A representation of the at least one CFE and at least one recommended remedial action is caused to transmit, via the processor, to at least one of a compute device of the first user or a compute device of a second user different from and associated with the first user.