I predict an uptick in crowdsourcing-based investigations, especially for tech accountability. As access to official APIs and research agreements decreases, crowdsourcing emerges out of necessity.

Newsrooms such as ProPublica and Correctiv have had teams specializing in engagement reporting and crowdsourcing for years now. Reporters have built browser extensions, set up nationally representative user panels, and shared web forms to figure out Uber and Lyft’s cut from rides and how Facebook continued to recommend partisan political groups despite telling lawmakers they had stopped.

More recently, reporters have requested user-exported data from TikTok and OpenAI to answer key questions about how tech users interact and what they’re shown. User-exported data contains detailed and well-formatted data based on each user’s history, and is likely an artifact of compliance to data privacy laws in Europe and California. Importantly, this data allows reporters to report on real behavior on tech platforms, as opposed to creating fictitious accounts and mimicking user behavior.

These stories lay the groundwork of what’s possible, but crowdsourcing is not without its limitations. Recruitment, non-representative samples, and the time needed to maintain these projects are unavoidable.

I am hopeful that technologists will build tools to help streamline data intake. However, participation will be crowdsourcing’s main bottleneck. Projects will need to be centered around addressing sources’ needs to truly succeed. This means tackling existing questions from members of a community where impartial and systematic evidence is lacking. This also means being transparent to sources about how their data will be used and anonymized (or not), and the risks involved.

I drank the crowdsourcing Kool-Aid after building a WhatsApp bot to investigate ride-share labor practices in New York. We met drivers where they are, and in return were trusted with a cache of interviews and evidence that upended the gig economy’s promise of flexibility.