The Minimum Wage as an Anchor: Effects on Determinations of Fairness by Humans and AI

TL;DR

This study investigates how minimum wage figures influence fairness judgments by humans and AI, revealing that minimum wages act as anchors that shift perceived fair wages, with notable differences for unrealistic values and AI responses.

Contribution

It demonstrates that minimum wages serve as anchors in fairness judgments for both humans and AI, highlighting differences in response patterns and the impact of unrealistic wage anchors.

Findings

Minimum wages influence fairness judgments by shifting responses toward the anchor.

Unrealistic high minimum wages cause responses to split into two modes.

AI responses are systematically lower and less anchored than human responses.

Abstract

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.