Irrelevant Alternatives Bias Large Language Model Hiring Decisions (2024)

Abstract: We investigate whether LLMs display a well-known human cognitive bias, the attraction effect, in hiring decisions. The attraction effect occurs when the presence of an inferior candidate makes a superior candidate more appealing, increasing the likelihood of the superior candidate being chosen over a non-dominated competitor. Our study finds consistent and significant evidence of the attraction effect in GPT-3.5 and GPT-4 when they assume the role of a recruiter. Irrelevant attributes of the decoy, such as its gender, further amplify the observed bias. GPT-4 exhibits greater bias variation than GPT-3.5. Our findings remain robust even when warnings against the decoy effect are included and the recruiter role definition is varied.

## introduction large language models (llms) are increasingly getting adopted in a wide range of industries to assist in decision-making for complex problems. entrusting decision processes to llms requires a comprehensive understanding of potential biases inherent in these models and implementing rigorous measures to minimize them. this is especially important for high-risk applications in industries such as human resources @xcite , where upholding essential human rights and ensuring fairness and accuracy in decision-making processes are crucial. the complexity of decision-making problems often arises from the need to evaluate numerous alternatives simultaneously. human judgements are known to be prone to various biases stemming from the composition of the choice set, known as context effects. one such well-documented and extensively studied cognitive bias is the attraction effect, also known as the asymmetric dominance effect @xcite ). an alternative is asymmetrically dominated (asd-ed) when it is inferior 1 the code is publicly available at https://github.com/ypencho/llm-attraction-effect . to one alternative (the target) in all attributes but only partially inferior to another alternative (the competitor). the attraction effect occurs when an asd-ed decoy alternative increases the likelihood of choosing the target, over a non-dominated competitor. the bias has been documented even if the decoy is not available for choice, a phenomenon known as the phantom decoy effect @xcite @xcite . adding a phantom decoy that is superior to the target and asymmetrically dominating (asd-ing) leads biased decision-makers to select the target more often than the non-dominated competitor. the possible positions of the asd-ed decoy and asd-ing phantom decoy alternatives are illustrated in figure 1 asymmetrically dominated (asd-ed) decoys by the target are positioned in the green region. the brown region corresponds to phantom decoys, which are asymmetrically dominating (asd-ing) the target. the map also shows the position of symmetrically dominated decoys and dominating phantom decoys by both the target and the competitor. dominated candidates might be considered in llm-assisted candidate selection tasks for a variety of reasons. first, irrelevant context passed to the llm through a recall maximising retrieval step of retrieval augmented generation (rag) might produce dominated candidates. second, biased retrieval towards sensitive attributes contributes to creating decoys, e.g., gender decoys @xcite . third, because of duplication in candidate records, an old cv might act as a decoy of the current cv if additional relevant qualifications and experience have been acquired. in addition to those organic origins of decoys among relevant candidates, this cognitive bias of (ai-)recruiters incentivises candidates to apply with one real and one fake inferior cv in order to increase their chance to be selected. if this possibility is recognised and exploited by candidates, it leads to an artificially expanded set of applicants with lower average level of qualifications, thus further complicating recruiters' task of evaluating and selecting the most suitable one. the attraction effect presents a violation of standard axioms of choice theory, thus implying that decision-makers do not have stable preferences. 2 nevertheless, it is a robust empirical finding, documented across multiple decision-making contexts and species, in particular in hiring decisions by human recruiters (see section 2 for a review). due to the unclear mechanism driving the attraction effect 3 , using llms as an aid in candidate selection decisions might mitigate or exacerbate the biased decision-making of human recruiters. this study is aimed at understanding whether llms suffer from the attraction effect in hiring decisions. to this end, we design a minimal experiment, following classical designs from the literature @xcite , and task gpt-3.5 and gpt-4 with a recruiter role. our findings show significant and consistent evidence of the attraction effect. the magnitude of the effect varies with the position of the decoy in the attribute space and with irrelevant attributes of the decoy such as its gender. although both models exhibit bias, gpt-4 shows significantly greater variation compared to gpt-3.5. our results are robust to including a warning against the decoy and varying the recruiter role definition. ## related literature this work contributes to three main strands of literature -cognitive biases of llms, decision-makers exhibiting the attraction effect, and biases in hiring decisions. ## experimental design overview and bias definition we adopt the general methodology of standard experiments on the attraction effect such as @xcite , whereby for each job opening, we establish two conditions: a control condition, where a hiring decision is made between a target and a competitor candidate, and a treatment condition, where a decoy candidate is introduced to the choice set. each candidate is defined by two relevant qualification values locating them in the two-dimensional alternatives space. more specifically, there is a trade-off between the target and the competitor in the sense that each of them is superior to the other with respect to one attribute as shown in figure 1 . we then prompt an llm to assume the role of a recruiter and task it with selecting the most qualified candidate. the bias resulting from the attraction effect is quantified as the difference in the probability for an llm to choose the target in both conditions. 5 5 there exist also alternative definitions of the attraction effect, see for example @xcite . according to the we say that there is evidence for the attraction effect if this difference is positive, thus implying that the presence of an irrelevant alternative biases the decision-making process. this measure of the attraction effect follows the standard definition used in the marketing research literature. a schematic overview of the experimental design is shown in figure 2 . we elaborate on each experimental feature below. prompt design figure 3 shows an example prompt. it starts by defining the role of a recruiter and includes instructions on fairness. the description of a candidate selection task follows: hiring a person for a specific job with two necessary qualifications. we consider six jobs across white-collar and blue-collar sectors, encompassing both stereotypically male and female occupations (see additionally, a requirement for a valid working permit, unrelated to skills nor experience, is included to allow for phantom candidates, i.e., such who are ineligible due to lacking a permit. the third part of the prompt defines the candidate choice set. there are two candidates in the control condition -target and control, and three candidates in the treatment condition -target, control, and decoy. the description of each candidate contains information on the following parameters: two qualification attribute levels, a possessive pronoun implying their gender, and the possession of a valid working permit. the latter is only negated for phantom decoy candidates. the prompt concludes with instructions requesting single token generations. ## conclusion we find evidence that hiring decisions made by llms such as gpt-3.5 and gpt-4 are influenced by asymmetrically dominated alternatives, similarly to human recruiters. we explore the placement of a decoy in the complete two-dimensional attribute space and find consistent patterns aligned with the classical attraction effect. we also study the effect of decoy gender and observe that it is most effective when aligned with the target. in general, gpt-4 presented much larger bias variation than gpt-3.5. we show that our results are robust to including a warning against the decoy and varying the recruiter role definition.