Structured Analytics Interface for Evidence-Based Hypothesis Review
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing analytic systems often discard relevant data during analysis due to filtering, exacerbating biases and leading to erroneous conclusions, which can have catastrophic results in fields like security and medical treatment.
Innovation Solution
An interactive structured analytic system that includes a display, analytics application, and modules for generating queries, evaluating data, and assessing hypotheses, allowing users to select and confirm evidence directly, thereby avoiding data filtering and offsetting cognitive biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If filters are used to remove irrelevant data, then data processing efficiency is improved, but relevant data is lost and analysis accuracy deteriorates
Solution Approach 1:
The system extracts only the essential filtering function while removing the harmful data loss effect. Users manually select which data to exclude from analysis, rather than applying automatic filters that indiscriminately remove potentially relevant information. This allows efficiency improvement through selective exclusion while preserving analysis accuracy by maintaining user control over data inclusion.
Solution Approach 2:
The system implements feedback mechanisms where users can review and adjust which data items are excluded from analysis. The system provides information about excluded data and allows users to modify their selections, ensuring that relevant data is not permanently lost. This feedback loop maintains reliability while still enabling efficient processing through user-defined exclusions.
2Loss of time
If automatic filtering is applied to data, then analysis time is reduced, but cognitive biases are exacerbated and conclusion reliability deteriorates
Solution Approach 1:
The system introduces an intermediary layer between automatic filtering and final analysis. This intermediary allows users to review, adjust, and validate filtering decisions before they impact the analysis. The intermediary mechanism preserves time efficiency by maintaining automated processing capabilities while preventing bias exacerbation through user oversight and control.
3Reliability
If comprehensive data is presented to users, then data relevance is improved, but information overload increases and analysis complexity deteriorates
Solution Approach 1:
The system segments the comprehensive data into organized, manageable categories and presentations. Rather than overwhelming users with all available data at once, the system divides information into structured segments that users can navigate systematically. This maintains data relevance by preserving comprehensive information while reducing analysis complexity through organized presentation and selective focus mechanisms.
Data Source
AI summary
An analytics system can include a display on which a plurality of images are shown, and an analytics application communicably coupled to the display. The analytics application can receive a question and hypotheses from a user using the display. The analytics application can also generate queries using a natural language module, and send the queries to a plurality of data sources. The analytics application can further receive data from the data sources in response to the queries, and evaluate the data to generate evaluated data. The analytics application can also present the evaluated data, and receive a selection of at least one data item of the evaluated data. The analytics application can further convert the at least one data item into evidence, receive a selection of the evidence applied to a hypothesis, and evaluate the hypothesis. The analytics application can also present an assessment that the hypothesis answers the question.