求职者是否仅因AI招聘中的错误纠正而重视程序？来自联合实验的证据
Do job seekers value procedure in AI hiring only for error correction? Evidence from a conjoint experiment
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中文总结 AI 辅助
通过联合实验发现，求职者重视AI招聘程序本身而非仅其纠错功能，人工参与影响最大，改进性能无法替代程序权利。
中文摘要 AI 辅助
雇主越来越多地将初步筛选委托给自动化系统，在许多情况下，申请在任何人阅读之前就被拒绝。对此类系统的接受程度可能既取决于其表现如何，也取决于产生决策的程序。以往的研究很少独立变化程序和表现，因此尚不清楚申请人重视程序本身还是其所纠正的错误。在一项预注册的配对档案联合实验中，1,919名美国求职者在八个选择中，对决策权限、错误率、解释、选择退出、申诉和独立偏见审计等独立随机化水平的系统进行选择。申诉、选择退出和偏见审计的价值并未随着错误拒绝变得更加普遍而上升，每个都保持在预注册的等价界限内。人工参与的分量超过任何程序特征，其影响陈述选择的程度与将错误拒绝从30%降至10%相当。这些模式限制了简单的错误纠正解释，并与申请人部分出于程序本身而重视程序相一致，因此改进系统性能并不能替代申请人可以援引的权利。
英文摘要
Employers increasingly delegate initial screening to automated systems, which in many cases reject an application before any human reads it. Acceptance of such systems plausibly depends both on how well they perform and on the procedure that produces the decision. Prior studies rarely vary procedure and performance independently, leaving it unclear whether applicants value procedure for its own sake or for the errors it corrects. In a preregistered paired-profile conjoint experiment, 1,919 United States job seekers made eight choices between systems with independently randomized levels of decision authority, error rate, explanation, opt-out, appeal, and independent bias audit. The value of the appeal, the opt-out, and the bias audit did not rise as wrongful rejections became more common, each staying within a preregistered equivalence bound. Human involvement carried more weight than any procedural feature, moving stated choice about as much as cutting wrongful rejections from 30% to 10%. These patterns constrain a simple error-correction account and are consistent with applicants valuing procedure partly for its own sake, so that improving a system's performance does not substitute for a right applicants can invoke.
发表机构
- London School of Economics and Political Science（伦敦政治经济学院）
- Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。
