I study the role of minimum wage as an anchor for judgements of the fairness of wages by both human subjects and artificial intelligence (AI). Through surveys of human subjects enrolled in the crowdsourcing platform Prolific.co and queries submitted to the OpenAI's language model GPT-3, I test whether the numerical response for what wage is deemed fair for a particular job description changes when respondents and GPT-3 are prompted with additional information that includes a numerical minimum wage, whether realistic or unrealistic, relative to a control where no minimum wage is stated. I find that the minimum wage influences the distribution of responses for the wage considered fair by shifting the mean response toward the minimum wage, thus establishing the minimum wage's role as an anchor for judgements of fairness. However, for unrealistically high minimum wages, namely $50 and $100, the distribution of responses splits into two distinct modes, one that approximately follows the anchor and one that remains close to the control, albeit with an overall upward shift towards the anchor. The anchor exerts a similar effect on the AI bot; however, the wage that the AI bot perceives as fair exhibits a systematic downward shift compared to human subjects' responses. For unrealistic values of the anchor, the responses of the bot also split into two modes but with a smaller proportion of the responses adhering to the anchor compared to human subjects. As with human subjects, the remaining responses are close to the control group for the AI bot but also exhibit a systematic shift towards the anchor. During experimentation, I noted some variability in the bot responses depending on small perturbations of the prompt, so I also test variability in the bot's responses with respect to more meaningful differences in gender and race cues in the prompt, finding anomalies in the distribution of responses.

翻译：本研究考察了最低工资作为人类被试和人工智能（AI）对工资公平性判断的锚定作用。通过在众包平台Prolific.co上招募人类被试进行问卷调查，并向OpenAI的语言模型GPT-3提交查询，我检验了当受访者和GPT-3被提示包含现实或非现实数值最低工资的额外信息时，与未声明最低工资的对照组相比，针对特定职位描述被认为公平的工资数值回应是否发生变化。研究发现，最低工资通过将平均回应向最低工资值偏移，从而影响了被认为公平的工资分布，确立了最低工资作为公平性判断锚定点的作用。然而对于非现实的高额最低工资（即50美元和100美元），回应分布分裂为两个明显模式：一个大致跟随锚定值，另一个虽整体向锚定值上移但仍接近对照组。锚定效应对AI机器人产生类似影响，但AI机器人认为公平的工资数值较人类被试呈现系统性下降偏移。对于非现实的锚定值，AI机器人的回应同样分裂为两个模式，但遵循锚定值的回应比例低于人类被试。与人类被试类似，AI机器人其余回应接近对照组，但也呈现向锚定值的系统性偏移。实验过程中，我注意到提示词微小扰动会导致机器人回应存在一定变异性，因此进一步检验了提示词中性别和种族线索等更具意义的差异对机器人回应变异性产生的影响，发现了回应分布中的异常现象。