Interactive Analytics Workflow for Evidence-Based Hypothesis Review
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Solution Overview
Problem
Existing analytic systems often discard relevant data and exacerbate biases, leading to erroneous or suboptimal 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, thereby offsetting cognitive biases and ensuring relevant data is considered.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If filters are used to remove irrelevant data, then data relevance is improved, but relevant data is removed and analysis accuracy deteriorates
Solution Approach 1:
The system implements an iterative feedback loop where users review and correct filter results, and the system learns from these corrections to improve future filtering. The feedback mechanism allows users to mark relevant data as irrelevant and vice versa, enabling the system to refine its filtering algorithms continuously, thus maintaining both data relevance and analysis accuracy.
Solution Approach 2:
The system dynamically adjusts filtering parameters based on user interactions and data characteristics. By changing filter sensitivity thresholds and inclusion criteria in response to user feedback and analysis context, the system optimizes the balance between removing irrelevant data and preserving relevant data, resolving the contradiction between data relevance and analysis accuracy.
2Productivity
If filters are used to remove irrelevant data, then processing efficiency is improved, but cognitive biases are exacerbated
Solution Approach 1:
The system introduces an intermediary layer of automated filtering that acts as a mediator between raw data and user analysis. This intermediary filter systematically removes obviously irrelevant data while preserving ambiguous or potentially relevant data for user review, thereby maintaining processing efficiency while preventing cognitive biases from influencing the initial data selection process.
Solution Approach 2:
The system enables users to perform self-correction of filter results, allowing them to override automated filtering decisions. This self-service mechanism empowers users to recognize and correct biased filtering while maintaining the efficiency benefits of automated processing, as users only need to intervene when the automated filter makes errors or introduces biases.
3Speed
If automated analysis is used, then analysis speed is improved, but user control over data selection is reduced
Solution Approach 1:
The system implements dynamic control where the level of automation adjusts based on user needs and data complexity. Users can switch between fully automated analysis mode for speed and interactive manual review mode for control, or combine both by having the system pre-filter data and then allowing user review of specific items. This dynamic adjustability resolves the contradiction between analysis speed and user control.
Solution Approach 2:
The system segments the data processing task into automated preliminary filtering and user-specific review phases. The automated component handles bulk data processing and obvious filtering, while users focus only on reviewing and validating specific data items that require human judgment, thus maintaining both speed through automation and control through selective user involvement.
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.