Zhuang Mike, Deschrijver Eliane, Ramsey Richard, Turel Ofir
School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, 3052, Australia.
School of Psychology, The University of Sydney, A18 Manning Rd, Camperdown, NSW, 2050, Australia.
Sci Rep. 2025 Mar 29;15(1):10894. doi: 10.1038/s41598-025-94631-9.
Although discrimination is typically believed to occur from well-defined categories like ethnicity, disability, and sex, studies have found that discrimination persists in minimal conditions lacking such categories. Participants have been found to preferentially allocate resources based on seemingly arbitrary shared characteristics such as dot estimation choices. Here, we use a preregistered experiment (n = 500) to investigate whether humans discriminate in a similar manner when interacting with artificial intelligence (AI) agents that ostensibly made dot estimations. We hypothesized that because humans harbor prejudice against algorithms relative to other humans (otherwise known as algorithm aversion), the strength of discriminatory behavior may be greater against AI than humans. Surprisingly, we found that participants distributed resources in a similar manner, albeit unequally, to both human and AI agents. Specifically, participants favored the other agent when decisions were aligned. Our findings suggest that discriminatory behavior is less influenced by the recipient's identity and more shaped by choice congruency.
尽管人们通常认为歧视源于种族、残疾和性别等明确的类别，但研究发现，在缺乏这些类别的极少情况下，歧视依然存在。研究发现，参与者会根据诸如点估计选择等看似随意的共同特征来优先分配资源。在此，我们进行了一项预先注册的实验（n = 500），以调查当人类与表面上进行点估计的人工智能（AI）代理交互时，是否会以类似的方式进行歧视。我们假设，由于相对于其他人，人类对算法怀有偏见（即所谓的算法厌恶），因此针对AI的歧视行为强度可能比对人类的更大。令人惊讶的是，我们发现参与者对人类和AI代理的资源分配方式相似，尽管并不平等。具体而言，当决策一致时，参与者更青睐另一方。我们的研究结果表明，歧视行为受接受者身份的影响较小，而更多地由选择一致性塑造。
