For decades, code contributions have been how open source projects learned who to trust. People would show up, do the work, take responsibility for their changes, and stick around. Over time, trust emerged from the work itself. AI tools have changed the economics of this very quickly. We use them ourselves every day, but a pull request no longer tells us as much as it used to about the person submitting it. A substantial patch used to imply substantial effort, and that effort was a reasonable proxy for good faith. That assumption no longer holds. For a browser, this matters. A browser runs untrusted input from the entire internet on the user’s machine, and one well-disguised vulnerability is all an attacker needs. We have already seen patient, well-resourced campaigns in open source to earn maintainer trust and abuse it. What has changed is how much faster and cheaper it has become to produce work that looks like a serious contribution.

all the flowers that cut through the earth, all, all the flowers are lost; everything is lost, everything is crossed with black, black upon black and worse than black, this colourless light.

A promising approach that addresses some worker output issues examines the way that workers do their work rather than the output itself, using machine learning and/or visualization to predict the quality of a worker’s output from their behavior [119,120]

Of the research foci, quality control has arguably received the most attention so far. Approaches for quality control largely fall into two camps: up-front task design and post-hoc result analysis. Task design aimsto design tasks that are resistant to low-quality work.

Unlike traditional organizations in which workers possess job security and managers can closely supervise and appropriately reward or sanction workers, or distributed computing systems in which processors are usually highly reliable, crowd work poses uniquechallenges for both workers and requestersranging fromjob satisfactiontodirection-setting, coordination, and quality control.