Generative AI Response Alignment Using Policy Feedback
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Solution Overview
Problem
Generative artificial intelligence (AI) systems often produce responses that are not adequately formatted, safe, ethical, or aligned with user-specific policies and regulations, leading to decreased user trust and confidence.
Innovation Solution
An AI optimizing system that analyzes responses from generative AI models based on user-specific and global policies, identifying alignment issues and refining responses to improve adherence to ethical and regulatory standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI models are used to produce responses quickly and conveniently, then productivity and ease of operation are improved, but the responses may not be adequately formatted, safe, or aligned with user-specific policies, leading to decreased reliability
Solution Approach 1:
An intermediary alignment analysis system is introduced between the generative AI model and the user. This system includes an alignment analyzer that evaluates responses against user-specific policies, global policies, and regulations, and an alignment improver that refines responses to meet alignment requirements without significantly impacting generation speed
Solution Approach 2:
A feedback loop is implemented where the alignment analyzer continuously evaluates generated responses and provides feedback to the alignment improver. The system iteratively refines responses until they meet the required alignment standards, ensuring reliability while maintaining productivity through efficient feedback mechanisms
2Reliability
If generative AI systems are aligned with multiple global, national, and industry policies, then reliability and safety are improved, but the complexity of the system increases due to multiple analysis requirements
Solution Approach 1:
The alignment analysis process is segmented into distinct modular components: an alignment analyzer that handles policy evaluation, an alignment improver that handles response refinement, and separate processing streams for different policy types (user-specific, global, national, industry). This modular architecture manages complexity while ensuring comprehensive policy adherence
3Productivity
If the generative AI model is trained to generate responses based on available data, then productivity is improved, but the responses may not be adequately formatted or safe, leading to loss of information quality
Solution Approach 1:
Alignment requirements including formatting standards and safety criteria are established and integrated into the response generation process in advance. The alignment improver applies pre-defined refinement rules and constraints before responses are delivered to users, ensuring high formatting quality without compromising data processing efficiency
Data Source
AI summary
A method may include providing a query and context associated with the query to a generative artificial intelligence model, in which the generative artificial intelligence model may be trained to generate a response to the query based on the context. The method may further include obtaining one or more policies, in which at least one of the one or more policies are specific to the user. An analysis of the response may be performed based on the one or more policies. Based on the analysis, alignment issues in the response may be identified. The response may be refined to improve the alignment issues.