Data Explainability Interface for Transparent Value Derivation
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Solution Overview
Problem
Computing systems often present complex data as a black box, making it difficult for users to understand how specific values are generated, leading to a lack of transparency and trust in the data presented.
Innovation Solution
A user interface is developed that provides a hierarchical structure and interactive chat interface to explain how values are calculated, allowing users to explore underlying values, historical data, and engage in conversational queries to gain insights into data derivation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complex data processing processes are used to generate data values, then data processing capability is improved, but data understandability deteriorates
Solution Approach 1:
The patent introduces an explanation generation module that acts as an intermediary between the complex data processing system and the user. This module generates human-readable explanations that bridge the gap between sophisticated processing algorithms and user comprehension, allowing complex processes to run while maintaining understandability through generated explanations of what the processes do and why they produce certain results.
2Loss of information
If detailed data processing information is provided to users, then data transparency is improved, but information processing time increases
Solution Approach 1:
The system implements partial action by providing explanations on-demand rather than displaying all processing information continuously. Users can request explanations for specific data values when needed, rather than being presented with comprehensive processing details for all data points, thus balancing transparency with efficient information delivery.
Solution Approach 2:
The explanation system is dynamic, adapting the level and type of information provided based on user interactions and context. The system generates explanations only when requested and can adjust the depth of explanation based on the specific data value and user needs, optimizing the balance between transparency and processing time.
3Quantity of substance
If comprehensive data structure is implemented, then data completeness is improved, but user interface complexity increases
Solution Approach 1:
The user interface is segmented into distinct functional areas: a data display area showing comprehensive data structures, and an explanation area that provides detailed information on-demand. This segmentation allows the complete data structure to be maintained while presenting information in manageable, contextually-relevant portions rather than overwhelming the user with all details simultaneously.
Data Source
AI summary
The present disclosure provides methods, systems, and devices providing explanations for the source of data in an interface. A system displays a first field in a data structure within a user interface, wherein the first field is automatically determined by the computing system based on a plurality of underlying parameters. The system displays an explanation user interface element in the user interface proximate to the first field. The system receives user input indicating selection of the explanation user interface element. The system, in response to receiving user selection of the explanation user interface element, updates the user interface to display one or more data objects representing the underlying parameters used to generate the first field.