论文标题
最低工资作为锚点：对人类和人工智能公平性确定的影响
The Minimum Wage as an Anchor: Effects on Determinations of Fairness by Humans and AI
论文作者
论文摘要
我研究了最低工资作为对人类受试者和人工智能（AI）工资公平性的判断的基础的作用。通过调查人类受试者的众包平台多产和疑问，提交给OpenAI语言模型GPT-3的查询，我测试是否认为，当受访者和GPT-3的数值响应是在受访者和gpt-3的提示中是否具有数值最小工资，无论是现实的，无论是现实的还是不现实的，都不是要在上面启动的，对特定职位描述的数值响应是否公平，相对于几乎不真实的是，相对性地相对得出的wage wage。我发现，最低工资通过将平均响应转换为对最低工资的平均响应，从而影响了被认为是公平的工资的响应分布，从而确立了最低工资的作用，作为公平判断的基础。但是，对于不切实际的最低工资，即$ 50和100美元，响应的分布分为两种不同的模式，一种大约遵循锚点，而一种则保持接近控制，尽管总体向上移动了锚。锚对AI机器人产生类似的影响。但是，与人类受试者的反应相比，AI机器人视为公平的工资表现出系统的下降。对于锚点的不切实际值，机器人的响应也分为两种模式，但与人类受试者相比，与锚的响应相比较小。与人类受试者一样，剩余的响应与AI机器人的对照组接近，但也表现出向锚点的系统转变。在实验过程中，我注意到机器人响应的一些可变性，具体取决于提示的小扰动，因此我还测试了机器人响应的变异性，相对于提示中的性别和种族线索的更有意义的差异，在响应分布中发现异常。
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.
