Adaptive Workflow Definition for Crowd-Sourced Task Quality Control
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Solution Overview
Problem
Crowdsourcing platforms face challenges in ensuring quality control due to the variability in worker behavior and skills, leading to inconsistent task outcomes, as they struggle to effectively manage a diverse and global labor pool.
Innovation Solution
A host server system that adapts workflow definitions and implements quality control mechanisms by utilizing worker profile data, intelligent test tasks, and virtual currency systems to evaluate and manage worker performance, ensuring quality and reliability in task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If crowd sourcing is used to complete tasks quickly and cost-effectively, then productivity increases, but quality and reliability of task outcomes deteriorate due to worker variability
Solution Approach 1:
The system performs preliminary actions by evaluating worker performance through test tasks before assigning actual tasks. Worker profiles are created and updated in advance based on test results, allowing the system to predict task completion quality before work begins. This preliminary evaluation ensures that tasks are assigned to suitable workers, maintaining reliability while preserving the speed benefits of crowd sourcing.
Solution Approach 2:
The system implements continuous feedback mechanisms by monitoring worker performance on actual tasks and updating worker profiles accordingly. Test tasks are used to evaluate workers, and the results feed back into the profile data. This feedback loop allows the system to adapt to worker performance changes and maintain high-quality outcomes while keeping the crowd sourcing model productive and scalable.
2Reliability
If quality control mechanisms are implemented to ensure task quality, then reliability improves, but system complexity increases due to the need for test tasks and worker profiling
Solution Approach 1:
The system achieves universality by creating a multi-functional worker profile that serves multiple purposes: storing worker information, evaluating performance, predicting task completion quality, and guiding task assignment decisions. Test tasks and actual tasks share the same evaluation framework. This universal approach consolidates quality control functionality into a single integrated system rather than requiring separate complex mechanisms, thereby improving reliability without proportionally increasing complexity.
3Measurement precision
If worker performance evaluation is conducted through test tasks, then measurement precision of worker quality improves, but loss of time increases due to additional task requirements
Solution Approach 1:
The system applies partial action by selecting only the most relevant and informative test tasks for each worker evaluation. Rather than administering comprehensive tests covering all possible skill areas, the system focuses on specific tasks that best predict performance on the types of work needed. This selective approach maintains measurement precision while minimizing the time investment required for evaluation.
Data Source
AI summary
Systems and methods of adaptive workflow definition of crowd sourced tasks and quality control mechanisms for multiple business applications are disclosed. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, for generating and distributing tasks to workers to optimize product categorization in an online marketplace, generating and distributing tasks to workers for search engine optimization of a website, and/or generating and distributing tasks to workers for business data verification and enhancement. Results can be aggregated from multiple workers to enhance or improve site information, site organization, or other business information to improve site placement in search results and/or to enhance/improve user experience.