Crowdsourcing concepts
Tap into the wisdom of the crowd on a large scale.
Keys to clean and accurate training data
Our methodology based on years of research and unique industry expertise can help you successfully tap into the wisdom of the crowd on a large scale. If you want to efficiently use the knowledge of thousands of people to get clean and accurate data for your ML needs, follow our tips for each of these essential steps.
1. Decomposition
Break your task down into the smallest possible steps and make each one a separate task.3. Interfaces
A good interface makes it easy for users to perform the same repeated actions quickly and correctly.4. Quality control
Carefully plan and configure a quality control system to ensure high-quality results.
Research Benchmarks
Use our examples as benchmarks to achieve the described levels of quality on popular research datasets.
Research papers
Browse through some of our Research team's latest work.
IMDB-WIKI-SbS: An Evaluation Dataset for Crowdsourced Pairwise Comparisons
Our dataset has balanced distributions of age and gender using the well-known IMDB-WIKI dataset as ground truth. We describe how our dataset is built and then compare several baseline methods, indicating its suitability for model evaluation.
Learn moreNeurIPS 2021CrowdSpeech & Vox DIY: Benchmark Dataset for Crowdsourced Audio Transcription
Domain-specific data is the crux of the successful transfer of machine learning systems from benchmarks to real life. Crowdsourcing has become one of the standard tools for cheap and time-efficient data collection for simple problems such as image classification: thanks in large part to advances in research on aggregation methods.
Learn moreNeurIPS 2021A General-Purpose Crowdsourcing Computational Quality Control Toolkit for Python
In this paper, we demonstrate Crowd-Kit, a general-purpose crowdsourcing computational quality control toolkit. It provides efficient implementations in Python of computational quality control algorithms for crowdsourcing, including uncertainty measures and crowd consensus methods.
Learn moreHCOMP 2021VLDB 2021 Crowd Science Challenge on Aggregating Crowdsourced Audio Transcriptions
This paper reviews the crowdsourced audio transcription shared task devoted to this problem and co-organized with the Crowd Science Workshop at VLDB 2021.
Learn moreVLDB 2021Prediction of Hourly Earnings and Completion Time on a Crowdsourcing Platform
We study the problem of predicting future hourly earnings and task completion time for a crowdsourcing platform user who sees the list of available tasks and wants to select one of them to execute.
Learn moreKDD 2020Text Recognition Using Anonymous CAPTCHA Answers
In this paper, we address the problem of labeling text images via CAPTCHA, where user identification is generally impossible. We propose a new algorithm to aggregate multiple guesses collected through CAPTCHA.
Learn moreWSDM 2020
Useful resources
Python library
We have an open-sourced library with a client that covers all API functionalities.Public datasets
Use our datasets for your projects or collect your own data that meets your needs.