Insight Creation Transparency for AI Trust
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Solution Overview
Problem
Existing digital organization strategies lack transparency in how insights are derived, making it difficult for organizations to trust and understand the mechanisms behind AI-driven decision-making.
Innovation Solution
A method for insight creation transparency that involves receiving a transparent insight query, extracting keywords, filtering a metadata graph to identify a node subset, generating a k-partite metadata graph, and creating a non-interactive query result to provide transparency on the insights and methods used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI-driven insights are generated using complex processing mechanisms, then the quality and depth of insights are improved, but the transparency and understandability of the derivation process deteriorate
Solution Approach 1:
The patent introduces an intermediary transparency layer that mediates between the complex AI processing mechanisms and the end users. This layer captures intermediate results, metadata, and processing traces during insight generation, then presents them in an understandable format. The intermediary preserves the complex processing for quality while translating it into transparent information for users, resolving the contradiction between insight quality and transparency.
2Loss of information
If detailed processing information is captured and presented, then transparency is improved, but the system complexity and computational overhead increase
Solution Approach 1:
The patent segments the transparency information into distinct components including intermediate results, metadata, processing traces, and confidence scores. Each segment serves a specific transparency function and can be independently managed and presented. This segmentation reduces overall system complexity by organizing transparent information into manageable parts rather than presenting all information uniformly.
Solution Approach 2:
The patent applies local quality by providing different levels and types of transparency information at different stages of the insight generation process. Critical intermediate results receive detailed documentation while less important processing steps receive summarized information. This localized approach to transparency reduces computational overhead by not uniformly processing all information at the same level of detail.
3Reliability
If comprehensive metadata and intermediate results are stored, then insight validation capability is improved, but the data storage requirements and processing time increase
Solution Approach 1:
The patent performs preliminary action by capturing and storing essential metadata and intermediate results during the insight generation process itself, rather than attempting to reconstruct or analyze the complete processing history afterward. This preliminary capture of validation-critical information enables faster validation since the necessary data is already prepared and organized, reducing the time penalty associated with comprehensive validation capabilities.
Data Source
AI summary
A method and system for insight creation transparency. Explainable artificial intelligence, in recent years, has become synonymous with a framework through which users, relying on machine learning models for various applications, may come to trust the result(s) outputted by said models through better comprehension of the mechanisms leading to said result(s). Leveraging a vast database of metadata for a plethora of unstructured and structured data/information, embodiments disclosed herein derive, or infer, insights therefrom that best address any user-submitted queries. Embodiments disclosed herein, further, provide transparency information detailing, for example, which input(s) and which technique(s) and/or algorithm(s) were employed to arrive at the derived/inferred insights.