Automated Crowd-Sourced Polling System with Bias Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional online polling methods lack efficiency in generating and administering polls, and often fail to provide statistically significant results due to selection biases and the inability to perform operations on crowd-sourced responses effectively.
Innovation Solution
A system that automates crowd-sourced polling by generating polls based on queries, submitting them to a crowdsourcing backend, retrieving responses, converting them to random variables, and performing operations such as statistical analysis and bias correction to obtain unbiased results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional online polling methods are used, then polls can be conducted to reach a wide audience, but the process lacks efficiency in generating and administering polls
Solution Approach 1:
The system enables automated poll generation where the computing system automatically creates polls from queries, administers them through crowdsourcing backends, and performs statistical analysis without requiring manual intervention at each step, thereby significantly improving productivity and reducing time loss
Solution Approach 2:
The system performs preliminary actions by automatically generating poll structures, selecting appropriate crowdsourcing backends, and pre-configuring statistical analysis parameters before polls are administered, which streamlines the overall process and reduces the time required for poll creation and analysis
2Quantity of substance
If crowd-sourced responses are collected, then a large volume of data can be obtained, but selection biases prevent statistically significant results
Solution Approach 1:
The system implements feedback mechanisms where crowd-sourced responses are continuously evaluated for bias, and the poll administration process is adjusted based on this feedback to maintain statistical significance. The manipulation component uses feedback from statistical analysis to refine data collection and processing
Solution Approach 2:
The system dynamically changes parameters such as sampling methods, population selection criteria, and statistical thresholds to compensate for selection biases while maintaining the volume of data collected, thereby preserving both quantity and measurement precision
3Measurement precision
If manual poll analysis is performed, then detailed examination of responses is possible, but user efficiency is reduced
Solution Approach 1:
The system replaces manual mechanical analysis with automated computational processing. The manipulation component automatically performs statistical operations, bias correction, and data synthesis on crowd-sourced responses, providing detailed analysis results without requiring manual intervention, thereby maintaining measurement precision while dramatically improving user efficiency
Data Source
AI summary
Various technologies described herein pertain to automation of crowd-sourced polling. At least one query can be received. The at least one query includes a request. A poll can be automatically generated based upon the at least one query, where the poll corresponds to the request. The poll can be submitted to a crowdsourcing backend, where instances of the poll are administered on the crowdsourcing backend. Moreover, crowd-sourced responses to the instances of the poll can be retrieved from the crowdsourcing backend. The crowd-sourced responses to the instances of the poll can respectively include crowd-sourced responses to the request. The crowd-sourced responses to the request can be converted to a random variable. An operation can be performed upon the random variable. The operation can include one or more of a statistical analysis (e.g., hypothesis testing), bias correction, an arithmetic operation, expected value computation, standard deviation computation, etc.