We often wish to target costly interventions (e.g. scholarships, financial aid, entrepreneurial loans) to the most suitable (e.g. hardworking, poor, trustworthy) individuals. However, suitability often remains unobserved and self-reports permit strategic misreporting. Peer prediction mechanisms, such as Robust Bayesian Truth Serum, aim to elicit suitability of an individual based on peer reports. However, these mechanisms are not robust to coordination among peers, which makes them unapplicable to digital screening problems. In this paper, we describe Algorithmic Truth Serum (ATS), the first peer prediction mechanism robust to coordination among peers. This mechanism would enable the automated, digital elicitation of peer information for the cost-effective targeting of interventions. To test this mechanism, we perform an experiment in which Indian job seekers digitally apply for a text transcription job. We elicit predictions from an applicant's peers and select the applicants with the highest predicted performance. To evaluate the performance of ATS, we compare eliciting peer predictions via ATS to not using incentives in an RCT. By improving screening, Algorithmic Truth Serum could aid in the growth of digital job market. Other possible applications include digital credit and targeting poverty programs.