Prolific helps the world’s leading AI companies evaluate and improve their models, and crowdsourcing platforms like ours power an estimated 50% of studies in behavioural sciences. This puts us at a unique intersection of AI development and academic research, where the integrity of the human data we generate has never carried more responsibility. This talk explores how our Data Science and Analytics team meets that responsibility through three layers of decision-making: strategic decisions where data science helps leadership navigate uncertainty; operational decisions where analytics keeps the marketplace running reliably; and algorithmic decisions where models act at a scale no human team could match. We bring each layer to life with real examples, from modelling participant supply and study fill rates, to building detection systems that identify AI-generated responses and protect the integrity of human data. Attendees will leave with a practical framework for anchoring data science work to organisational purpose, and concrete examples of what that looks like in practice.
Technical Level of Session: High Level/overview
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