AI Interaction Governance Layer for Hallucination and Bias Control
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Solution Overview
Problem
Conventional AI models suffer from inaccuracies and biases due to reliance on training data, leading to incorrect outputs and a lack of intrinsic understanding of correctness, with a focus on hallucinations obscuring the need for a model-agnostic interaction-design-oriented approach.
Innovation Solution
Implement a system that facilitates user interaction with AI models through guided sessions, leveraging context information to manage bias and enhance output accuracy by tailoring interactions to specific users or contexts, and incorporating attribution metadata to improve model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models rely on training data to generate outputs, then they can function based on available data, but they produce hallucinated outputs and lack intrinsic understanding of correctness
Solution Approach 1:
The patent introduces an interaction layer as an intermediary component between the user and the AI model. This layer manages the interaction workflow, validates inputs and outputs, and coordinates with the model to reduce hallucinations while maintaining the model's ability to generate outputs based on training data.
Solution Approach 2:
The system implements feedback mechanisms where the interaction layer monitors model outputs, validates them against constraints and context, and provides feedback to guide the model toward more accurate responses. This feedback loop helps reduce hallucinations by continuously adjusting the interaction based on output quality.
2Reliability
If focus is placed on model development and training to solve hallucination, then model accuracy may improve, but implementation complexity and cost increase
Solution Approach 1:
Instead of making the AI model itself more complex through additional training or architectural changes, the patent introduces a separate interaction layer that handles validation, constraint enforcement, and workflow management. This keeps the model relatively simple while adding complexity only where needed for governance.
Solution Approach 2:
The system segments the AI interaction into distinct components: the AI model for generation, the interaction layer for governance and validation, and the user interface for interaction. This segmentation allows each component to be optimized independently, reducing overall implementation complexity compared to making the entire system more complex.
3Reliability
If conventional AI models are used without context management, then interactions are simpler, but outputs lack accuracy and are biased
Solution Approach 1:
The interaction layer performs preliminary actions by establishing context, constraints, and validation rules before the AI model generates outputs. This preliminary setup ensures accuracy and reduces bias without significantly complicating the user experience, as these preparations happen transparently in the background.
Solution Approach 2:
The interaction layer serves multiple functions including context management, validation, constraint enforcement, and workflow coordination, all within a single unified component. This multi-functionality maintains interaction simplicity for users while internally managing the complexity needed for accurate and unbiased outputs.
Data Source
AI summary
Systems and methods are provided to facilitate user interaction with AI models. For example, guided sessions turn user requests into workflow steps, that enable optimization at each step. In other aspects, an interaction layer or component can be configured to manage interactions with AI models that are tailored to the requesting user (e.g., via curation agents or components) or tailored to a context identified during an interactive session. These interactive/guided sessions expand on the functionality to account for and/or encompass perspectives associated with source and/or generated content. Many AI models are known to be biased among other issues. The system can manage bias using context information. Leveraging context information, the system provides insight and associated context to eliminate the bias from outputs returned, and/or even enhance the bias of outputs returned as desired, among other options. AI models can be provided as curators having specific characteristics that constrain outputs.