Interdependency of Data Collection & Crowdsourcing
In today’s digital age, crowdsourcing has played a major role in redesigning the landscape of how data collection is done. The crowdsourcing approach brings together various kinds of highly skilled individuals from various walks of life and geographies, breaking the traditional barriers of outreach. This helps us build a team of experts under one roof without being bound by traditional overheads and limitations.
Let’s understand the relation between Crowdsourcing & Data Collection in depth and take a deep dive into the value it offers to develop the Machine Learning (ML) & Artificial Intelligence (AI) Algorithms.
The Crowdsourcing Benefit for Data Collection
Data Collection & Crowdsourcing have a symbiotic relationship where the crowd participants help to collect diverse datasets which cut through mixed demographics, crucial for building robust & complex ML & AI algorithms. These ML techniques, in turn, help refine and optimize crowdsourcing workflows, thereby enhancing efficiency & improving the quality of outputs.
What Makes Crowdsourcing Models Versatile?
Crowdsourcing is an extremely flexible model that can be customized to fit varying project requirements & dynamics. Here are some examples which showcase its versatility:
Example 1: Microtasks
In this approach, a complex task is broken down into multiple smaller and easy-to-execute micro tasks and these are distributed among participants based on their skill segmentation. This model helps to complete the tasks at a faster pace as more people join hands, ensuring higher accuracy by assigning each type of microtask to the right skilled participant.
Example 2: User Studies
Crucial in research and development in fields involving ML/AI, health-tech, product designing, etc., user studies leverage crowdsourcing to gather diverse participant feedback, leading to better products and services. These studies involve a method where a device (prototype, prerelease, off the shelf, etc.) or software (new release, beta, legacy, etc.) is explored in a real-life scenario based on specified protocols with diverse range of participants. Crowdsourcing is a perfect approach to offer mixed demographics leading to further refinement of algorithms, products, and services.
Example 3: Simple Data Collections
These include simple protocols like audio, images, videos, or text which can be easily & efficiently executed through crowdsourcing at an extremely justifiable price point. Depending upon the quality & compliance requirements of the project, one can choose the crowd execution model. For projects needing stringent quality and compliance, a fully managed crowd model selects participants based on specific qualification criteria and ensures close-looped coordination/management at every stage. For a less critical project, it’s best to opt for a hybrid crowd model and for the projects prioritizing volume churn over stringent quality and compliance, an unmanaged model can be leveraged using a pull mechanism.
These crowd delivery models have improved the delivery execution methods to enhance reliability, robustness, cost-effectiveness, and flexibility as per the delivery needs, especially for the complex tasks. Additionally, popular crowdsourcing platforms such as Populii enable sourcing candidates with the right skill sets from diverse backgrounds and helping clients to execute projects with accurate data and saving costs.
What’s the Key to Success in Crowdsourcing?
“Pay Right & Pay Fair” is the first mantra that leads to success in crowdsourcing. Success is further supported by the concept of community and building a community with outreach campaigns, clear project objectives, transparency, recognition, providing a support system, and driving community engagement. Having a widely accepted payment system helps the crowd to receive their payments easily and motivates them to continue contributing to data collection projects.
Payoneer is one of the payment partners for Populii, making global payments easier than ever. The seamless payment system allows gig workers to focus on work with assurance of timely payments. It helps build a positive and conducive environment to building something revolutionary in the world of AI & ML through microtasks & user studies. The experience and exposure gained out of this by the gig workers adds to their resume as well as knowledge, thus empowering them on their personal front as well.
What’s the influence of Expert Intervention on Data Quality?
The Lean Principle of “Do it right at the first time” plays a crucial role in eliminating waste & workflow optimization. While crowdsourcing focuses on bringing in diversity of participants, the experts ensure that the data is collected in the right manner right from the source. They positively influence the data quality by doing spot checks, providing guidance in time or while data collection is in process. This increases the confidence levels in the accuracy of data and makes it more relevant and reliable for future analysis and enhancements.
Summary:
Crowdsourcing-driven data collections (their symbiotic interdependency) offer high accuracy, reliability, scalability, and cost-effectiveness in the execution of projects that include ML/AI systems. The convergence of Machine Learning, Artificial Intelligence, and Crowdsourcing presents fertile ground for innovation and collaboration. By harnessing the collective intelligence of crowdsourced communities and leveraging AI and ML techniques, organizations can accelerate the data collection processes, improve data quality, and unlock new possibilities in various domains. As we navigate this evolving landscape, continuous research, innovation, and collaboration will be paramount to ensure responsible and equitable utilization of these delivery approaches and in unlocking the full potential of the symbiotic relationships of Data Collection with Crowdsourcing.
