With MIT Sloan associate professor John Horton, Wiles conducted an online experiment in which some employers were given access to an AI assistant to help them draft job posts. In a\u00a0working paper<\/a> detailing the results, they share that the AI tool does make the job-posting process more efficient, but those gains are overshadowed by\u00a0costs to job seekers in the form of low-quality job postings and wasted time.\u00a0<\/p>\n More job posts, but not more jobs\u00a0<\/p>\n The experiment involved a website designed to help businesses fill remote work positions \u2014 jobs like computer programming, copy editing, or graphic design, ranging from short gigs for a few hundred dollars to longer term, full-time assignments.<\/p>\n During the period of the experiment, employers that joined the website to post jobs for the first time were randomly given the option of using AI to help draft their post. In those cases, the companies would summarize what they were looking for in two sentences, and AI (the GPT-3.5 Turbo large language model) would write a first draft of the full job post, which employers could then edit.<\/p>\n 1 In an online study, job posts written with AI were 15% less likely to lead to a hire. <\/p>\n Roughly 75% of employers chose to use AI if it was offered, and most of them accepted the draft copy without making edits to it. This process cut the time required to write posts by 44%, on average. From the perspective of the employers, the AI proved quite useful, and it made them 19% more likely to post a job.\u00a0<\/p>\n Although the number of postings increased, the researchers did not find an increase in actual job matches. In fact, job posts written with AI were 15% less likely to lead to a hire.\u00a0<\/p>\n \u201cWhen we made it this easy to write job descriptions, we found that it encouraged employers with no real interest in hiring to post jobs \u2014 employers that simply wanted a way to get a sense of the applicant pool, to see who\u2019s out there,\u201d Wiles said.\u00a0<\/p>\n The researchers also found that the job posts themselves were less informative when drafted by AI. Rather than creating a post asking for a programmer with four or more years of experience who knows Python and Julia, the AI would generate something more generic, such as a post looking for an experienced go-getter with a background in software engineering.<\/p>\n The result of these two effects was a less efficient hiring market. Though more jobs were posted, the number of positions being filled did not increase \u2014 in part because some employers had no intention of hiring, and in part because the more generic posts led to poorer matches between the people applying and the job.<\/p>\n \u201cWhile the technology saved employers a lot of time, it ended up wasting job seekers\u2019 time,\u201d Wiles said. \u201cEmployers may have an incentive to use it, but, from a central planner\u2019s perspective, this is a costly tool to introduce to the marketplace.\u201d<\/p>\n
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