The QROWD platform enables the integration of human and crowd intelligence in the data lifecycle, from generation to analytics. It offers tools to design and deploy pipelines that bring together the best data science and AI techniques of the day with responsible, yet scalable approaches to harness the wisdom of the crowds.
Design your pipeline – QROWD’s participatory framework [link] provides guidance on a range of human in the loop approaches you can use alongside data science and AI algorithms. It helps you understand how you could motivate and engage different audiences to help you collect data, label data for machine learning, or validate the results of algorithms.
Collect data – transform the way you gather and update information by mobilising large crowds through bespoke tools. Reach out to people to gather feedback through mobile apps and crowdsourcing platforms. Reward contributions. Ensure data quality and keep track of its provenance.
Validate algorithms – design experiences and tools to collect human judgements to your data by interfacing data pipelines to the crowdsourcing services you need.
Link data sources – bring together different datasets automatically, and ask the crowd to confirm challenging cases.
Analyse data to detect trends and patterns – connect human in the loop and human computation services to your analytics to bootstrap algorithms and solve challenging cases.
Be mindful of privacy and data protection – use data protection by design and reward data contributions fairly
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