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PopResume: Causal Fairness Evaluation of LLM/VLM Resume Screeners with Population-Representative Dataset
Sumin Yu, Juhyeon Park, Taesup Moon
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Summary
PopResume is a population-representative dataset and causal fairness auditing framework for LLM/VLM-based resume screening. It decomposes hiring outcome disparities into 'business necessity' (job-relevant qualifications) and 'redlining' (demographic proxies) paths using Path-Specific Effects (PSE), addressing limitations in existing benchmarks that rely on manual attribute injection.
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PopResume → enables → Path-Specific Effect
confidence 95% · PopResume... enabling path-specific effect (PSE)-based fairness evaluation.
Path-Specific Effect → decomposes → Hiring Bias
confidence 92% · We decompose the effect of a protected attribute on resume scores into two paths: the business necessity path... and the redlining path
LLM → usedfor → Resume Screening
confidence 90% · The use of large language models (LLMs) in resume screening and hiring decisions is expanding rapidly
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Abstract
Abstract:We present PopResume, a population-representative resume dataset for causal fairness auditing of LLM- and VLM-based resume screening systems. Unlike existing benchmarks that rely on manually injected demographic information and outcome-level disparities, PopResume is grounded in population statistics and preserves natural attribute relationships, enabling path-specific effect (PSE)-based fairness evaluation. We decompose the effect of a protected attribute on resume scores into two paths: the business necessity path, mediated by job-relevant qualifications, and the redlining path, mediated by demographic proxies. This distinction allows auditors to separate legally permissible from impermissible sources of disparity. Evaluating four LLMs and four VLMs on PopResume's 60.8K resumes across five occupations, we identify five representative discrimination patterns that aggregate metrics fail to capture. Our results demonstrate that PSE-based evaluation reveals fairness issues masked by outcome-level measures, underscoring the need for causally-grounded auditing frameworks in AI-assisted hiring.
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- Source: https://arxiv.org/abs/2603.22714v1
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PopResume: Causal Fairness Evaluation of LLM/VLM Resume Screeners with Population-Representative Dataset Sumin Yu 1 * , Juhyeon Park 2 * , Taesup Moon 1,2,3† 1 ECE, Seoul National University, 2 IPAI, Seoul National University, 3 ASRI / INMC / AIIS, Seoul National University ysmsoomin, parkjh9229, tsmoon@snu.ac.kr Abstract We present PopResume,a population- representative resume dataset for causal fairness auditing of LLM- and VLM-based resume screening systems. Unlike existing benchmarks that rely on manually injected demographic information and outcome-level disparities,PopResume is grounded in population statistics and preserves natural attribute relationships, enabling path-specific effect (PSE)-based fairness evaluation. We decompose the effect of a protected attribute on resume scores into two paths: the business necessity path, mediated by job-relevant qualifications, and the redlining path, mediated by demographic proxies.This distinction allows auditors to separate legally permissible from impermissible sources of disparity. Evaluating four LLMs and four VLMs on PopResume’s 60.8K resumes across five occupations, we identify five representative discrimination patterns that aggregate metrics fail to capture.Our results demonstrate that PSE-based evaluation reveals fairness issues masked by outcome-level measures, underscoring the need for causally-grounded auditing frameworks in AI-assisted hiring. 1 Introduction The use of large language models (LLMs) in re- sume screening and hiring decisions is expanding rapidly (Lo et al., 2025; Gan et al., 2024), rais- ing growing concerns regarding fairness and dis- crimination (Cherepanova et al., 2025; Wilson and Caliskan, 2024; Wilson et al., 2025). Given the potential for automated decisions to produce sys- tematic disparities that disadvantage individuals based on protected attributes such as gender or race, organizations deploying automated hiring sys- tems are now expected to demonstrate compliance with anti-discrimination law (U.S. Congress, 1964; * Equal contribution. † Corresponding author. 85.25 Male avg. 85.25 Female avg. Our screener is fair! Well, it’s not indeed! TENDENIEBIERIE NIE =BIE+RIETENDE Redlining (RIE) -impermissible Business necessity (BIE) -permissible ! "# $% • Focus only on outcome disparity • Disrupt real-worldvariable relationships Injected name (only name changed) UNFAIR Limitations Current Evaluation Scheme Path-Specific Effect-Based Evaluation Population-Representative Resume Dataset TXT JPGJPG Female-typical Name stateCA eduBachelor’s exp5 yrs Male-typical Name stateCA eduBachelor’s exp5 yrs ACS PUMS SSACensusPSID Figure 1: Prior works inject protected attributes, making causal framework–based evaluation infeasible, and mea- sure outcome disparity; our population-representative resume dataset instead enables causal effect–based eval- uation and pathway decomposition. U.S. Equal Employment Opportunity Commission, 1979; Qing and Lim, 2022). In actual audit and compliance environments, group-level outcome disparities are often identified using simple statistical criteria such as selection rate comparisons (U.S. Equal Employment Oppor- tunity Commission, 1979). While these metrics serve as usefulearlyindicators of potential risk, theydo not explain the mechanisms behind the dis- parities. This limitation is critical, as legal liability ultimately depends not only on the presence of dis- parity, but also on how it arises. Recent studies investigating bias in LLM-based hiring systems (Iso et al., 2025; Hu et al., 2025; Wang et al., 2024) share the same limitation: they primarily focus on statistical disparities between demographic groups, without identifying the underlying causal pathways through which protected attributes influence hiring outcomes, as illustrated in Fig. 1 (left). However, legal and policy frameworks around employment discrimination hinge on a crucial distinction: dis- parities arising through legitimate job-related path- ways,e.g.,role-essential qualifications, may be per- missible (business necessity), whereas disparities 1 arXiv:2603.22714v1 [cs.CY] 24 Mar 2026 driven by proxy variables such as name or address that can disadvantage specific protected groups are unacceptable (redlining). Therefore, meaningful fairness auditing requires understanding not just the magnitude of outcome disparities, but the causal pathways through which protected attributes pro- duce them. Path-specific effect (PSE)-based fairness frame- works (Plecko and Bareinboim, 2024; Pearl, 2009; Chiappa, 2019) offer a principled approach to this challenge, enabling answeringhowandthrough which pathwaysprotected attributes contribute to observed disparities.However, enabling such pathway-level analysis requires data with explicit control over all relevant attributes, such as pro- tected attributes, legitimate qualifications, and proxy variables, simultaneously. Existing bench- marks for LLM-based hiring systems fail to meet this requirement in two distinct ways. Real-world resume datasets (Hamner et al., 2012; Senger et al., 2025) typically do not contain ground-truth demo- graphic information about applicants, as such at- tributes are withheld due to privacy concerns. Cur- rent evaluation benchmarks (Hu et al., 2025; Wang et al., 2024; Iso et al., 2025; Armstrong et al., 2024; Wilson and Caliskan, 2024; Nghiem et al., 2024), on the other hand, often manually inject and perturb demographic information, which can disrupt the realistic relationships between attributes, making causal framework-based evaluation infeasible. We address these gaps by introducing legally- grounded causal fairness auditing framework for LLM- and vision language model (VLM)-based resume screening systems. Building on the PSE framework, we decompose the causal effect of a protected attribute on resume scores into two path- ways: (1) thebusiness necessitypath, mediated by job-relevant qualifications (education, work expe- rience), and (2) theredliningpath, mediated by demographic proxies (name, address). This de- composition enables auditors to identify not only that a hiring system discriminates, but how and whether the discrimination is legally defensible. To support this analysis under realistic conditions, we constructPopResume, a dataset derived from U.S. population-level statistics (e.g.,ACS PUMS (U.S. Census Bureau, 2023)) that preserves the natural relationships among attributes and enables causal effect estimation, which contains 60.8k resumes across five occupations. Our contributions are as follows. • We propose a PSE-based fairness auditing frame- work for LLM/VLM-based resume screening systems that separates legally permissible out- come disparities mediated by job-relevant quali- fications from legally impermissible ones trans- mitted through demographic proxies. • We construct a large-scale resume dataset grounded in U.S. population statistics, preserv- ing realistic distribution and enabling causal effect-based fairness evaluation. 2 Preliminaries First, we review the legal foundations of employ- ment discrimination law that motivate our fairness criteria (Sec. 2.1), and then introduce the causal modeling framework and PSEs that underpin our analysis (Sec. 2.2). 2.1 Legal Background Title VII of the US Civil Rights Act (U.S. Congress, 1964) prohibits employment discrimination based on race, color, religion, sex, and national origin, which are referred to asprotected attributes. The legal theory typically applied to automated deci- sions is thedisparate impactdoctrine (Jones, 2022; Barocas and Selbst, 2016), which holds that even a seemingly neutral practice can be unlawful if it produces outcomes that disproportionately disad- vantage a protected group. To establish disparate impact, plaintiffs must first demonstrate a disparity in outcomes between groups, commonly assessed using statistical heuristics such as the Four-Fifths rule. This step identifies thepresenceof a disparity but does not determine its causal origin. Critically, however, disparate impact analysis does not end with identifying statistical disparities. Under the burden-shifting framework established by Title VII, employers may justify the observed disparity by demonstrating that it arises from job-related crite- ria consistent withbusiness necessity, recognizing that disparities mediated through legitimate, job- relevant factors, such as professional qualifications or required skills, are permissible. Accordingly, le- gal liability hinges on a fundamentally causal ques- tion: whether the observed disparity arises through permissible pathways (business necessity) or im- permissible ones (redlining). This motivates the PSE-based analysis that can distinguish between these two types of effects. 2 ! " # $ (a) Standard Fairness Model (SFM). ! Mediator W " # $ % (b) Mediator decomposition in our framework. Figure 2: In our framework, the mediator setWis decomposed into business necessity componentsBand redlining-related componentsR. Blue edges denote BIE pathways, while red edges denote RIE pathways. 2.2 Standard Fairness Model & Causal Effects We use notations(X,X,x,x)to denote random vectors, random variables, and corresponding real- izations, respectively. Standard Fairness Model (SFM).To formal- ize the causal relationships between protected at- tributes and hiring outcomes, we build on the struc- tural causal model (SCM) framework (Pearl, 2009). An SCM induces a causal graphG, where nodes correspond to variablesV, directed edges represent causal relationships, and dashed bi-directed edges indicate hidden confounders. Following Plecko and Bareinboim (2024), we adopt the standard fairness model (SFM) with V=X,Z,W,Y. SFM partitions variables into four groups: a protected attributeX, an outcome Y, confoundersZthat are not causally affected by X, and mediatorsWthat may be causally influ- enced byX, as depicted in Fig. 2a. This abstraction allows fairness analysis without requiring the com- plete causal graph to be specified, while preserving the causal pathways through which the protected attributeXmay influence the outcomeY. Path-specific Effects (PSE).Under the SFM, the influence of the protected attributeXon the out- comeYcan propagate through multiple pathways. To quantify these effects, we adopt the potential out- come (Pearl, 2009). Given variablesX,Y⊆V, theaverage potential outcomeE[Y x ]denotes the expected value ofYifXwere set toxby interven- tion.Total effect(TE) of changingXfromx 0 to x 1 is defined as TE(x 1 ,x 0 )≜E[Y x 1 ]−E[Y x 0 ],(1) capturing the overall causal effect ofXonY through all causal pathways. TE can be decom- posed into thenatural direct effect(NDE) and the natural indirect effect(NIE). NDE captures the por- tion of the effect ofXonYthat isnotmediated through the mediatorsW, NDE(x 1 ,x 0 )≜E[Y x 1 ,W x 0 ]−E[Y x 0 ],(2) while NIE captures the effect transmitted through the mediatorsW, NIE(x 1 ,x 0 )≜E[Y x 1 ]−E[Y x 1 ,W x 0 ].(3) 3 PSE-based Fairness Evaluation 3.1 Mediator Decomposition As discussed in Sec. 2.1, legal doctrines such as redliningandbusiness necessityrequire distinguish- ing the causal pathways through which protected attributes influence hiring outcomes. To capture this distinction within the causal framework, we decompose the mediator setWinto two compo- nents:B, capturing job-relevant qualifications as- sociated withbusiness necessity, andR, capturing demographic proxies associated withredlining, as illustrated in Fig. 2b 1 . Based on this decomposi- tion, we introduce two PSEs, termed theB-specific indirect effect(BIE), and theR-specific indirect ef- fect(RIE). Intuitively, BIE captures the portion of the causal effect transmitted throughB, while RIE captures the portion transmitted throughR, corre- sponding to the pathways highlighted by the blue and red edges in Fig. 2b. BecauseBandRmay causally influence each other, theorder of interven- tionson these mediators is not uniquely defined. Specifically, when considering BIE, two interven- tion orders are possible: (i) fixingRatR x 0 and intervening onB, or (i) first intervening onRfrom R x 0 toR x 1 and then intervening onB. Therefore, we adopt a symmetric formulation that averages over the two possible mediator intervention orders. Formally, BIE is defined as BIE(x 1 ,x 0 )≜ 1 2 h E[Y x 1 ]−E[Y x 1 ,B x 0 ,R x 1 ,R x 1 ] | z (i) +E[Y x 1 ,B x 1 ,R x 0 ,R x 0 ]−E[Y x 1 ,B x 0 ,R x 0 ] | z (i) i . (4) 1 While this decomposition is inspired by legal doctrines in employment discrimination, the normative interpretation of these pathways depends on legal and societal contexts. 3 μ i π i i= 3E[Y|B,R,X,Z] P(X=x 0 |R,Z) P(X|R,Z) 1[X=x 1 ] P(X=x 0 |Z) i= 2E[μ 3 (B,R,x 1 ,Z)|R,X,Z] P(X=x 0 |R,Z) P(X|R,Z) 1[X=x 1 ] P(X=x 0 |Z) i= 1E[μ 2 (R,x 1 ,Z)|X,Z] 1[X=x 0 ] P(X|Z) Table 1: Definition of nuisance parameters. Similarly, RIE is defined as RIE(x 1 ,x 0 )≜ 1 2 h E[Y x 1 ]−E[Y x 1 ,B x 1 ,R x 0 ,R x 0 ] +E[Y x 1 ,B x 0 ,R x 1 ,R x 1 ]−E[Y x 1 ,B x 0 ,R x 0 ] i . (5) By construction, NIE is decomposed into the sum of BIE and RIE,i.e.,NIE(x 1 ,x 0 ) = BIE(x 1 ,x 0 ) +RIE(x 1 ,x 0 ). 3.2 PSE Estimation Estimation of PSE.To estimate the PSEs defined in Eqs. (1) to (5), we need to es- timate five quantities from the given data, E[Y x 0 ],E[Y x 1 ],E[Y x 1 ,W x 0 ],E[Y x 1 ,B x 0 ,R x 1 ,R x 1 ] , andE[Y x 1 ,B x 1 ,R x 0 ,R x 0 ]. The estimation of the first four quantities are already discussed by Zhang et al. (2025). Therefore, we detail the estimation of E[Y x 1 ,B x 1 ,R x 0 ,R x 0 ] , firstly introduced in our work. Following the DML-UCA algorithm of Jung et al. (2024), a doubly robust estimator with finite sample guarantee can be constructed as follows. Lemma 1(DML-UCA, (Jung et al., 2024)).Given a sampleD i.i.d. ∼P(V), doubly robust estimator ˆ ψ forE[Y x 1 ,B x 1 ,R x 0 ,R x 0 ]constructed with following procedure has finite sample guarantee: 1.Take anyL-fold random partition of the dataset D≜(V 1 ,...,V n ),D= S L ℓ=1 D ℓ where |D ℓ |=n/L. 2. For eachℓ= 1,2,...,L, construct nuisance parameter estimators ˆ μ= ( ˆ μ 1 , ˆ μ 2 , ˆ μ 3 )and ˆ π= ( ˆ π 1 , ˆ π 2 , ˆ π 3 ) usingD ℓ , and compute ˆ ψ ℓ ≜E D ℓ φ(V; ˆ μ, ˆ π),whereμ,πare outlined in Tab. 1 and φ(V; ˆ μ, ˆ π)≜ˆπ 3 Y−ˆμ 3 (B,R,X,Z) + ˆπ 2 ˆμ 3 (B,R,x 1 ,Z)−ˆμ 2 (R,X,Z) + ˆπ 1 ˆμ 2 (R,x 1 ,Z)−ˆμ 1 (X,Z)+ ˆμ 1 (x 0 ,Z). 3.The estimator ˆ ψ is the average of ˆ ψ ℓ L ℓ=1 , ˆ ψ≜ 1 L ( ˆ ψ 1 +·+ ˆ ψ L ). Mathematical details are provided in Appen- dices C.1 to C.3, and the finite sample guarantee is inherited from Theorem 2 in Zhang et al. (2025). Resume AttributeOperational VariableData Source X,ZGender, Race, Agegender, race, ageACS PUMS BWork experienceexp_yearPSID BEducationedu_levelACS PUMS RNamefirst_name,surnameSSA, Census RAddressstateACS PUMS Table 2: Mapping between conceptual variables and their operational representations. We estimate the nuisance parameters using XG- Boost (Chen and Guestrin, 2016); corresponding hyperparameter search details are provided in Ap- pendix C.5. Grouping of Categorical Variables.The estima- tion process requires estimatingE[Y|·]andP(X|·). However, in practice, some discrete variables may have a large number of categories, which can lead to unstable estimation when rare categories are un- evenly represented across partitions. To address this issue, we group the categories of such vari- ables into super-categories, reducing the number of categories and enabling more reliable estimation. The specific variables and grouping strategies used in our dataset are described in Appendix C.4. 4 PopResume Estimating the PSEs introduced in Sec. 3 requires access to all variables inV, including the pro- tected attributeX, confoundersZ, and mediators BandR. However, as discussed in Sec. 1, pub- licly available resume datasets typically lack an- notations for protected attributes and other rele- vant covariates. To address this limitation, we con- structPopResume, a population-representative re- sume dataset grounded in U.S. population statistics. The dataset approximates the natural joint distribu- tion ofV\Y, preserving realistic relationships among the variables. Fig. 3 illustrates the overall dataset construc- tion pipeline, which consists of two stages. First, we generate population-representative structured profiles from multiple statistical sources ( 1 - 2 in Fig. 3, Sec. 4.1). Second, each profile is converted into a realistic natural-language resume through rule-based instantiation ( 3 in Fig. 3, Sec. 4.2). Before introducing our dataset construction pipeline in detail, we clarify how resume attributes are operationalized in our structured representation. Tab. 2 summarizes the mapping between concep- tual variables, resume attributes, and their opera- tional variables in dataset construction. While the mapping ofXandZis straightforward, the me- 4 Resume Realization Text Resume Image Resume w/o Profile Photo Image Resume with Profile Photo Population Statistics LLM/VLMResume Scoring Path-Specific Effect-Based genderrace age edu_level exp_year state first_name surname job score: 85.25 . . . 60,884 structured profiles TE NDE NIE RIEBIE TE NDE NIE RIEBIE ! gender raceage edu_level exp_yearstate first_name surname job Rule-based Contents Generation LLM/VLM Pop ❶ ❸ ❹ ❺ Evaluation TE NDE NIE BIERIE NIE= BIE+ RIE TENDE ! "# $% % & ! "#$% (( *+, , , , % & " % & # ! " ! Resume Population-RepresentativeStructured Profiles ❷ ≈( / $ ⋅( / % ⋅( / & ⋅( / ' TXT JPG JPG Job Description Input: Job Description + Resume Output: Suitability Score (0–100) Scoring Prompt namestate exp year edu level ageracegender namestate exp year edu level ageracegender namestate exp year edu level ageracegender namestate exp year edu level ageracegender namestate exp year edu level ageracegender &#'(&'( Face-MoGLE Figure 3: Pipeline for constructing the population-representative resume dataset and evaluating LLM/VLM-based resume screeners. 1 Estimation of joint distributionP(X,Z,B,R|J)based on Assumption 1. 2 Population- representative structured profiles consisting of protected attributeX, confounderZ, business necessity mediators B, and redlining mediatorsR. 3 Resume realization, where each structured profile is converted into a natural- language resume using rule-based procedures conditioned solely on assigned attributes, eliminating uncontrolled variation. Three formats are produced: text resumes for LLM evaluation, and resume images with and without synthesized profile photos for VLM evaluation. 4 Resume scoring by LLM/VLM screeners, which assign a score Y given a job description and a resume. 5 Path-specific effect-based evaluation, estimating TE, NDE, and NIE, and further decomposing NIE into BIE and RIE. diator variablesBandRdo not appear directly in population statistics, although they conceptu- ally correspond to resume components such as work experience, education, name, and address. In- stead, we represent them using observable variables that can be reliably obtained from public datasets. Specifically, name is represented using sampled first_nameandsurname, and address is represented at the state level (state). Work experience is repre- sented asyears of experience(exp_year), and edu- cation as a categoricaleducation level(edu_level). These operational variables allow us to represent both job-related qualifications and demographic proxy signals within a unified structured profile. We note that the categorization of variables into BandRreflects one plausible operationalization; alternative categorizations are possible depending on the application context. 4.1 Population-Representative Dataset To approximate realistic applicant distributions, we construct structured resume profiles grounded in U.S. population survey data. Our objective is to approximate the joint distribution for each jobJ, i.e.,P(X,Z,B,R|J), which captures demo- graphic and job-related characteristics of applicants within each occupation. Because available sta- tistical sources provide only partial demographic and occupational information rather than a unified resume-level dataset, directly obtaining the full joint distribution is not feasible. Therefore, we factorize the joint distribution as follows. We then estimate each component using different source datasets and perform ancestral sampling based on the ACS PUMS (U.S. Census Bureau, 2023), a national survey of the U.S. population. Assumption 1.First_name depends only on gen- der, age, surname depends only on race, and the source datasets used to estimate the components are mutually consistent. Based on Assumption 1, the following holds. P(V\Y|J) =P(X,Z,state,edu_level|J) | z P 1 ·P(exp_year|X,Z,state,edu_level,J) |z P 2 · P(first_name|gender, age) | z P 3 ·P(surname|race) | z P 4 ˆ P 1 from ACS PUMS.We first obtain samples fromP 1 for each occupation using the 2023 ACS PUMS (U.S. Census Bureau, 2023), a large-scale population survey that provides individual-level demographic, geographic, and occupational infor- mation for the U.S. population. We then denote the empirical distribution of these samples as ˆ P 1 . Specifically, we restrict the ACS PUMS popula- tion to individuals who are currently employed and possess valid occupation codes. This ensures that the sampled individuals represent plausible job applicants in the labor market. To ensure suf- ficient statistical support within each occupation, 5 we sort jobs by sample size and randomly select five occupations from the top twenty most repre- sented categories. For each selected occupation, we sample individuals to obtaingender,race,age, state, andedu_level. We restrict the age range to 18≤age≤44to focus on early- and mid-career workers who constitute the primary pool of active job seekers while excluding minors who are subject to legal employment restrictions. ˆ P 2 from PSID.In the next step, to generate realistic exp_yearvalues, we leverage data from the Panel Study of Income Dynamics (PSID) (Social Re- search Center, 2025), a longitudinal household sur- vey that tracks employment histories, income, and demographic characteristics of individuals in the United States. Our goal is to approximate theP 2 . To account for the distribution shift in marginal dis- tribution between PSID and ACS PUMS, we adopt density ratio matching (Sugiyama et al., 2012) to reweight samples from PSID. We train an XGBoost regressorf(·)(Chen and Guestrin, 2016) to approx- imate the mean of the conditional distribution under the following assumption. Assumption 2.P 2 follows truncated normal dis- tribution,P 2 ∼TruncNormal f(·),σ,[0,M] . whereTruncNormal(μ,σ,[0,M])denotes trun- cated normal distribution with meanμ, standard deviation ofσ, and support of[0,M]. Notably, the σis estimated hierarchically based onage,gender, andJ, derived from the residuals of the training samples. In addition,M=age−18. To check the validity of this assumption, we visualized the Q-Q plot in Appendix B.2, and confirm that the normality assumption is acceptable. ˆ P 3 from SSA & ˆ P 4 from Census.We obtain the empirical distribution ˆ P 3 from the Social Security Administration (SSA) name records (Social Secu- rity Administration, 2025), which provides yearly counts of first names by gender. First names are sampled with conditioning on gender and age to reflect gender-specific naming patterns and pre- serve generational naming trends. Then, ˆ P 4 is ob- tained from the 2010 U.S. Census surname statis- tics (U.S. Census Bureau, 2016), which provide race-conditioned surname frequencies. Names sam- pled from these distributions serve as observable demographic signals within resumes and do not introduce additional dependencies beyond the spec- ified conditioning variables. 4.2 Resume Generation Pipeline After constructing the structured profiles contain- ing all attributes(X,Z,B,R)for each jobJ, we convert each profile into a natural-language resume. The overall resume structure is informed by pub- licly available resume templates from LiveCareer 2 . Resume content is generated using rule-based pro- cedures conditioned solely on structured variables. This design eliminates unintended variation caused from uncontrolled textual randomness, ensuring that any differences in model outputs can be at- tributed to(X,Z,B,R). For example, education entries are instantiated according toedu_level, with graduation year determined by age and degree type. School names, majors, and bullet descriptions are sampled from job-specific pools of fictional insti- tutions and content. Similarly, contact informa- tion (e.g., phone numbers and email addresses) is synthetically generated and does not introduce additional dependencies beyond the structured vari- ables. The detailed rules for instantiating each resume entry are provided in Appendix B.1. In total, the dataset contains 60,884 resumes across five jobs. Detailed dataset statistics are provided in Appendix B.3. Additionally, we also consider a resume scor- ing scenario in which resumes are accompanied by profile photos and evaluated using a vision language model (VLM). To support this setting, we generate synthetic profile photos conditioned on demographic attributes,i.e., gender,race, and age. Specifically, we use Face-MoGLE (Zou et al., 2025) to generate images with prompts “a profes- sional headshot portrait photo of a age year old race gender, white background, formal attire, front-facing”. We validate whether the generated images correctly reflect the specified attributes, as detailed in Appendix B.5. Example resumes rendered in three formats–text resumes, image re- sumes without profile photos, and image resumes with profile photos–are illustrated in Fig. 8. 5 Experiments 5.1 Experimental Setup Models.We evaluate four LLMs for text-based resume scoring: Llama-3.1-8B-Instruct (Grattafiori et al., 2024), Mistral-7B-Instruct-v0.2 (Jiang et al., 2023), GPT-4o-mini (Hurst et al., 2024), and Gemini-2.5-Flash-Lite (Comanici et al., 2025). For 2 https://w.livecareer.com/ 6 image-based resume evaluation, we consider four VLMs: Qwen2.5-VL-7B-Instruct (Bai et al., 2025), InternVL2-8B (Chen et al., 2024), GPT-4o (Hurst et al., 2024), and Gemini-2.5-Flash-Lite. Protected Attributes.While our framework is agnostic to the choice of protected attributes, we focus ongenderin the main experiments and pro- vide results forracein Appendix E. In the fol- lowing experiments, we setx 0 =Femaleand x 1 =Male. Namely, a positive effect value indi- cates that the model favors male candidates relative to female candidates through the corresponding pathway, whereas a negative value indicates the opposite. Scoring.Each resume is evaluated alongside a job description of the target occupation, and the model is instructed to assess how suitable the ap- plicant is for that specific job and assign a score (Y) from 0 (least suitable) to 100 (most suitable). We note that our framework can also support alter- native scoring rules, such as the cosine similarity between embeddings of job descriptions and re- sumes, adopted by Resume Matcher 3 . Evaluation Protocol.For LLM-based evalua- tion, we consider a practical setting in which the demographic information is not explicitly present in resumes. For VLM-based evaluation, we con- sider two scenarios: one using a text-only resume image as input, and another using a resume image with an attached profile photo. Based on the model outputs, PSEs in Sec. 3 are estimated using DML- UCA withL= 5. We report 95% confidence inter- vals obtained from 500 bootstrap samples. PSE is consideredsignificant(̸= 0) if its confidence inter- val excludes zero andnegligible(≃0) otherwise. We consider5×3×2 = 30configurations span- ning five occupations, three resume formats, two protected attributes and evaluate four models for each configuration, resulting in total 120 evaluation cases. Full results are reported in Appendix E. 5.2 When Do Different Pathways Matter? To understand how demographic attributes influ- ence model decisions through different causal path- ways, we analyze the estimated causal effects across all experimental configurations. Based on the PSEs, we identify several representative dis- crimination scenarios, presented in Fig. 4, corre- 3 https://github.com/srbhr/Resume-Matcher TENDENIEBIERIE 1.5 0.0 1.5 Effect Value Registered Nurses Gemini-2.5-Flash-Lite (LLM) (a) Case 1 TENDENIEBIERIE 2 0 2 Effect Value Construction Laborers Mistral-7b-instruct-v0.2 (b) Case 2 TENDENIEBIERIE 0.6 0.0 0.6 1.2 Effect Value Elementary Middle School Teachers Llama-3.1-8b-instruct (c) Case 3 TENDENIEBIERIE 0.4 0.0 0.4 0.8 Effect Value Registered Nurses Qwen2.5-vl-7b-instruct (d) Case 4 TENDENIEBIERIE 1 0 1 Effect Value Accountants and Auditors Qwen2.5-vl-7b-instruct (e) Case 5 (cancellation) TENDENIEBIERIE 1 0 1 2 Effect Value Accountants and Auditors GPT-4o (f) Case 5 (amplification) Figure 4: Representative examples of five cases based on our causal decomposition. sponding to a distinct auditing interpretation re- garding the role of protected attributes in scoring. Case 1: Direct Discrimination without Explicit Demographic Information.In this case, NDE ̸= 0even though demographic information isnot explicitly present in the resumes. This indicates that the model can infer protected attributes from other variables in the resume, such as names or edu- cation levels, and directly incorporate this inferred information into the scoring process. Out of the 80 evaluation cases, in which protected attributes are absent from resumes, 49 fall into this category. This finding suggests that simply removing explicit demographic information from resumes is insuffi- cient to prevent discrimination, as models can still infer such attributes and use them in their decisions. Case 2: Discrimination Masked by Cancellation. In this case, TE≃0despite NDE̸= 0and NIE ̸= 0, with NDE·NIE<0. That is, the direct and indirect effects operate in opposite directions and largely cancel out, making TE≃0. Therefore, relying solely on TE, or equivalently, on outcome- based fairness metrics such as disparate impact, would lead an auditor to incorrectly conclude that no discrimination is present and hence the system is fair, while our framework reveals that substantial causal effects still exist. We observe that 6 out of 7 120 evaluation cases fall into this case. Case 3: Disparities Driven by Legitimate Quali- fications.In this case, NDE≃0with RIE≃0, yet BIE̸= 0, indicating that the observed disparity is transmitted primarily through job-relevant quali- fications such as education levels and experience years. Under the disparate impact doctrine, such disparities may be considered legally defensible if the employer can demonstrate business necessity. This case highlights the importance of distinguish- ing qualification-mediated disparities from proxy- based discrimination when auditing hiring systems. 6 out of 120 evaluation cases fall into this case. Case 4: Discrimination through Demographic Proxies.In this case, NDE≃0and BIE≃0, yet RIE̸= 0, meaning the disparity arises through demographic proxy variables, such as names and address. These variables can encode demographic information of protected attributes, enabling the model to indirectly incorporate those signals into the scoring process. This pathway is legally im- permissible under Title VII, as it corresponds to the redlining doctrine: using geography- or name- based signals as proxies for protected attributes. Our decomposition allows auditors to isolate and identify this pathway regardless of whether the to- tal effect is large or small. We observe that 6 out of 120 evaluation cases fall into this case. Case 5: Disparities Driven by Mixed Mediation Pathways.In this case, both BIE and RIE are sig- nificant (i.e.,BIE̸= 0and RIE̸=0), contributing to NIE. This suggests that when the model assigns scores to resumes, it utilizes both job-related quali- fications and demographic proxy signals simulta- neously. Notably, these pathways can amplify or cancel out the magnitude of NIE, thereby poten- tially obscuring any conflicting mechanisms. This case demonstrates the significance of decompos- ing mediators into qualification-related (B) and proxy-related components (R), as proposed by our framework. If BIE and RIE are not separated, vari- ous causal mechanisms may not be distinguishable at the level of NIE, which could limit the audi- tor’s ability to determine whether the observed gap arises from legitimate qualification factors or from discriminatory proxy variables. We observe that 53 out of 120 evaluation cases fall into this case. TENDENIEBIERIE 1.5 0.0 1.5 Effect Value w/ profile photos w/o profile photos Accountants and Auditors Gemini-2.5-Flash-Lite (VLM) TENDENIEBIERIE 1.0 0.5 0.0 0.5 Effect Value w/ profile photos w/o profile photos Registered Nurses GPT-4o Figure 5: PSEs in VLM-based resume scoring. 5.3 Effect of Profile Photos on VLM-based Resume Scoring Since image-based resumes may include profile photos in realistic scenarios, we generate synthetic profile photos conditioned on each applicant’s de- mographic attributes and attach them to the re- sumes. Based on two variants of resumes, image re- sumes with and without profile photos, we evaluate resume scoring VLM models and estimate the cor- responding PSEs. This allows us to examine how the presence of profile photos influences the causal pathways through which demographic attributes af- fect model scores. Two representative examples are shown in Fig. 5. When profile photos are included, we observe that the magnitude of NDE increases noticeably. Across 20 pairs of evaluation cases, 8 exhibit this pattern. This suggests that profile photos make demographic attributes more directly accessible to the model, enabling it to infer pro- tected attributes more easily and reflect them in the scoring process, highlighting an additional risk in image-based resume screening. Organizations deploying VLM-based resume screening systems should therefore carefully assess whether profile photos are necessary for the hiring task. When visual information is incorporated, additional audit- ing or monitoring procedures may be required to ensure that demographic signals do not introduce unintended bias into automated scoring pipelines. 6 Conclusion In this work, we presentedPopResume, a population-representative resume dataset and a 8 PSE-based causal fairness auditing framework for LLM/VLM-based resume screening systems. Us- ing PopResume and the newly introduced PSEs, BIE and RIE, our framework enables auditors to distinguish legally permissible from impermissible sources of disparity, a distinction that outcome- level metrics fundamentally cannot make. Through extensive evaluations across diverse configurations, we identified five representative discrimination pat- terns that aggregate fairness metrics fail to capture. Our results further demonstrate that profile photos can introduce an additional direct discrimination risk in VLM-based evaluation. We hope PopRe- sume serves as a foundation for causally-grounded, legally-informed auditing of AI-assisted resume screening systems. Limitations Our work primarily focuses on constructing a re- sume dataset based on U.S. population statistics. Therefore, the applicability of our dataset to other countries or demographic distributions may be lim- ited. However, we believe that our procedure is general and can be used to construct similar re- sume datasets tailored to different populations or contexts. In addition, although our dataset is designed to preserve realistic relationships between attributes based on population statistics, the resumes them- selves are synthetically generated using rule-based procedures. As a result, certain aspects of real- world resumes, such as nuanced career trajectories, writing styles, or unstructured information, may not be fully captured. Despite these limitations, our approach provides a controlled and transparent environment that en- ables causal pathway–based fairness evaluation, which is difficult to achieve using existing real- world or manually perturbed benchmarks. We therefore view our dataset as a complementary tool that facilitates systematic analysis of fairness in LLM/VLM-based resume screening systems. Ethical Considerations In this work, we analyze fairness in resume screening systems through the lens of causal path- ways, distinguishing between redlining-related and business-necessity-related pathways following le- gal and policy discussions in employment discrimi- nation. However, determining whether a particular pathway should be considered fair or unfair is ulti- mately a normative question that depends on legal, societal, and contextual considerations. The cate- gorization adopted in this work should therefore not be interpreted as a definitive or universally ap- plicable judgment about fairness. Instead, our goal is to provide an analytical framework that enables auditors to examine how different causal pathways contribute to observed disparities in automated hiring systems. By de- composing the total effect into interpretable com- ponents, our approach aims to support more trans- parent and structured discussions about potential sources of discrimination. We believe that such analyses can serve as a useful tool for researchers, practitioners, and policymakers when assessing the fairness implications of AI-assisted hiring systems, even though the final determination of fairness re- mains context-dependent. We also note that the specific categorization of resume attributes intoBandRreflects one opera- tionalization motivated by legal and policy discus- sions, and may vary across jurisdictions, industries, or hiring contexts. Our framework does not require a fixed categorization. Rather, alternative attribute groupings can be defined and their causal effects assessed accordingly. References Lena Armstrong, Abbey Liu, Stephen MacNeil, and Danaë Metaxa. 2024. The silicon ceiling: Auditing gpt’s race and gender biases in hiring. InProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, pages 1–18. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wen- bin Ge, Sibo Song, Kai Dang, Peng Wang, Shi- jie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, and 8 others. 2025. Qwen2.5-vl technical report.arXiv preprint arXiv:2502.13923. Solon Barocas and Andrew D Selbst. 2016. Big data’s disparate impact.Calif. L. Rev., 104:671. Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. InProceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785– 794. Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, and 1 others. 2024. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. InProceedings of 9 the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24185–24198. Valeriia Cherepanova, Chia-Jung Lee, Nil-Jana Akpinar, Riccardo Fogliato, Martin Bertran Lopez, Michael Kearns, and James Zou. 2025. Improving llm group fairness on tabular data via in-context learning.Pro- ceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8(1):579–590. Silvia Chiappa. 2019. Path-specific counterfactual fair- ness.Proceedings of the AAAI conference on artifi- cial intelligence, 33(01):7801–7808. Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Mar- cel Blistein, Ori Ram, Dan Zhang, Evan Rosen, and 1 others. 2025. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities.arXiv preprint arXiv:2507.06261. Chengguang Gan, Qinghao Zhang, and Tatsunori Mori. 2024. Application of llm agents in recruitment: a novel framework for automated resume screening. Journal of Information Processing, 32:881–893. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. The llama 3 herd of models.arXiv preprint arXiv:2407.21783. Ben Hamner, Road Warrior, and Wojciech Krupa. 2012. Job recommendation challenge.https://kaggle. com/competitions/job-recommendation.Kag- gle. Yuhan Hu, Ziyu Lyu, Lu Bai, and Lixin Cui. 2025. Fair- work: A generic framework for evaluating fairness in llm-based job recommender system. InProceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 3964–3968. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, and 1 others. 2024. Gpt-4o system card.arXiv preprint arXiv:2410.21276. Hayate Iso, Pouya Pezeshkpour, Nikita Bhutani, and Estevam Hruschka. 2025. Evaluating bias in llms for job-resume matching: Gender, race, and education. InProceedings of the 2025 Conference of the Na- tions of the Americas Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (Volume 3: Industry Track), pages 672–683. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Men- sch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guil- laume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023. Mistral 7b.arXiv preprint arXiv:2310.06825. Colin Clemente Jones. 2022. Systematizing discrimi- nation: Ai vendors & title vii enforcement.U. Pa. L. Rev., 171:235. Yonghan Jung, Jin Tian, and Elias Bareinboim. 2024. Unified covariate adjustment for causal inference. In Advances in Neural Information Processing Systems, volume 37, pages 6448–6499. Curran Associates, Inc. Frank P-W Lo, Jianing Qiu, Zeyu Wang, Haibao Yu, Yeming Chen, Gao Zhang, and Benny Lo. 2025. Ai hiring with llms: A context-aware and explainable multi-agent framework for resume screening. InPro- ceedings of the Computer Vision and Pattern Recog- nition Conference, pages 4184–4193. Huy Nghiem, John Prindle, Jieyu Zhao, and Hal Daumé Iii. 2024. “you gotta be a doctor, lin”: An investigation of name-based bias of large language models in employment recommendations. InPro- ceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 7268– 7287. Judea Pearl. 2009.Causality. Cambridge university press. Drago Plecko and Elias Bareinboim. 2024. Causal fair- ness analysis: A causal toolkit for fair machine learn- ing.Foundations and Trends in Machine Learning, 17(3):304–589. Yap Jia Qing and Ernest Lim. 2022. A legal frame- work for artificial intelligence fairness reporting.The Cambridge Law Journal, 81(3):610–644. Elena Senger, Yuri Campbell, Rob Van Der Goot, and Barbara Plank. 2025. Karrierewege: a large scale career path prediction dataset. InProceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 533–545. Social Research Center. 2025. Panel study of income dynamics, public use dataset. Social Security Administration. 2025. Baby names from social security card applications.https://w.ssa. gov/oact/babynames/. Accessed 2025. Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori. 2012.Density ratio estimation in machine learning. Cambridge University Press. U.S. Census Bureau. 2013.Census regions and divisions of the united states.https: //w2.census.gov/geo/pdfs/maps-data/ maps/reference/us_regdiv.pdf. U.S. Census Bureau. 2016.Frequently oc- curringsurnamesfromthe2010census. https://w.census.gov/topics/population/ genealogy/data/2010_surnames.html.Ac- cessed 2025. 10 U.S. Census Bureau. 2023. American community sur- vey 1-year public use microdata sample. Accessed: 2025. U.S. Congress. 1964. Civil rights act of 1964, Title VII. Pub. L. No. 88-352, 78 Stat. 241. Codified at 42 U.S.C. § 2000e et seq. U.S. Equal Employment Opportunity Commission. 1979. Questions and answers to clarify and provide a common interpretation of the Uniform Guidelines on Employee Selection Procedures. 44 Fed. Reg. 11996. Accessed: 2025. Jointly issued by EEOC, DOJ, DOL, and OPM. Ze Wang, Zekun Wu, Xin Guan, Michael Thaler, Adri- ano Koshiyama, Skylar Lu, Sachin Beepath, Ediz Ertekin, and Maria Perez-Ortiz. 2024. Jobfair: A framework for benchmarking gender hiring bias in large language models. InFindings of the Associa- tion for Computational Linguistics: EMNLP 2024, pages 3227–3246. Kyra Wilson and Aylin Caliskan. 2024.Gender, race, and intersectional bias in resume screening via language model retrieval. InProceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 7, pages 1578–1590. Kyra Wilson, Mattea Sim, Anna-Maria Gueorguieva, and Aylin Caliskan. 2025. No thoughts just ai: biased llm recommendations limit human agency in resume screening.arXiv preprint arXiv:2509.04404. Junzhe Zhang and Elias Bareinboim. 2018. Fairness in decision-making—the causal explanation formula. Proceedings of the AAAI conference on artificial in- telligence, 32(1). Kevin Zhang, Yonghan Jung, Divyat Mahajan, Karthikeyan Shanmugam, and Shalmali Joshi. 2025. Path-specific effects for pulse-oximetry guided de- cisions in critical care. InThe Thirty-ninth Annual Conference on Neural Information Processing Sys- tems. Xuechao Zou, Shun Zhang, Xing Fu, Yue Li, Kai Li, Yushe Cao, Congyan Lang, Pin Tao, and Junliang Xing. 2025. Mixture of global and local experts with diffusion transformer for controllable face generation. arXiv preprint arXiv:2509.00428. The Use of Large Language Models (LLMs) We utilized LLMs for the purpose of polishing our manuscript only. A Extended Related Works A.1 Fairness in LLM-based Hiring Systems The use of LLMs for resume screening and can- didate evaluation has rapidly increased, enabling automated assessment of applicant suitability in hir- ing pipelines. However, recent studies have raised concerns about fairness and demographic bias in such systems, showing that LLM outputs may vary depending on protected attributes such as gender and race. Hu et al. (2025); Wang et al. (2024) investigate demographic bias in LLM-based hiring by explic- itly representing protected attributes and then per- turbing them to observe the resulting changes in scores or rankings. To better reflect practical set- tings where demographic attributes are not explic- itly stated, Nghiem et al. (2024); Iso et al. (2025); Armstrong et al. (2024); Wilson and Caliskan (2024) inject and perturb protected attribute infor- mation through names that are typically associated with specific genders or races. While these approaches can reveal the presence of demographic disparities, they do not distinguish the causal pathways through which such disparities arise. Moreover, because demographic information is manually injected into each resume, the original relationships between protected attributes and other variables are disrupted, making causal-framework- based evaluation infeasible. Our work addresses these limitations by in- troducing a causal framework that enables path- specific analysis of demographic effects in LLM- and VLM-based resume scoring systems, while generating resume datasets that preserve the natu- ral relationships between protected attributes and other variables. A.2 PSE-Based Algorithmic Fairness Path-specific effect (PSE)–based fairness has emerged as a flexible paradigm for auditing and mitigating discrimination in algorithmic decision- making (Zhang and Bareinboim, 2018; Chiappa, 2019; Plecko and Bareinboim, 2024). The cen- tral idea is to distinguish fair from unfair causal pathways linking a sensitive attribute to a decision: effects transmitted through legitimate mediators are considered permissible, whereas those propagating through designated unfair pathways are restricted or removed. Within this framework, Zhang et al. (2025) re- cently examined how race-mediated discrepancies in pulse oximetry measurements propagate through clinical decision pathways in intensive care units (ICUs). However, to the best of our knowledge, this framework has not yet been applied to auditing the fairness of LLM/VLM-based resume screening 11 systems. B Additional Details on the Resume Dataset Construction B.1 Instantiation Rules Each structured applicant profile is determin- istically converted into a natural-language re- sume using rule-based instantiation procedures. The generation process constructs the resume sections–education, work history, and contact information–directly from the structured variables (X,Z,B,R). Education.Education entries are generated from the variableedu_level. The number of education entries and bullet descriptions depend on the edu- cation level. For example, Bachelor’s, Associate’s, and high school levels produce a single entry, while advanced degrees such as Master’s or Doctorate produce two entries, consisting of a Bachelor’s de- gree followed by the higher degree. Institution names are sampled from a pool of fictional schools corresponding to each degree level, and majors are sampled from occupation-specific major pools. Graduation years are determined based on age and degree type to ensure consistency with realistic educational timelines. Work History.Work history entries are gen- erated from the predicted years of experience (pred_exp). The number of work roles is deter- mined by a piecewise rule based on experience years. For example, individuals with less than three years of experience are assigned a single role, while individuals with more than ten years of experience may have up to four roles. Employment periods are constructed sequentially starting from the end of education, and role durations are distributed across the total experience length. Each role includes a company name sampled from occupation-specific company pools and responsibility descriptions sam- pled from job- and seniority-specific bullet pools. Role seniority (junior, mid, senior) is determined based on the temporal order of positions. Name, Address, and Contact Information. Names and addresses originate from the struc- tured variables sampled during dataset construction. State names are converted into their corresponding two-letter USPS abbreviations when rendered in resumes. Phone numbers and email addresses are synthetically generated and do not correspond to Table 3: Number of structured applicant profiles gener- ated for each occupation in PopResume. OccupationNum Registered Nurses17,632 Elementary and Middle School Teachers16,188 Software Developers13,370 Accountants and Auditors7,784 Construction Laborers5,910 Total60,884 real individuals. Email addresses are constructed from the individual’s name, and phone numbers follow standard U.S. phone number formatting. Resume Formatting.The final resume text is generated by assembling the sections into a stan- dardized format consisting of a header (name, loca- tion, and contact information), followed by skills, work history, and education sections. This deter- ministic template ensures that variation in the gen- erated resumes arises solely from the underlying structured attributes rather than uncontrolled tex- tual randomness. B.2 Q-Q plot for Assumption 2 We visualize the Q-Q plot to validate the normality assumption in Assumption 2. As shown in Fig. 6, the empirical quantiles closely follow the reference line, indicating that the normality assumption is reasonably satisfied. 0.00.20.40.60.81.0 Theoretical quantiles 0.0 0.2 0.4 0.6 0.8 1.0 Ordered Values Q-Q Plot for Assumption 2 Figure 6: Q-Q Plot for Assumption 2. B.3 Detailed Dataset Statistics PopResume contains 60,884 structured applicant profiles spanning five occupations: registered nurses, elementary and middle school teachers, 12 software developers, accountants and auditors, and construction laborers. Tab. 3 summarizes the num- ber of profiles generated for each occupation. Each profile is subsequently rendered into multiple re- sume formats used in the evaluation. Fig. 7 summarizes key statistical properties of the constructedPopResumedataset. These statis- tics illustrate the demographic composition of the dataset. (a) and (b) show the gender and race com- positions across occupations. Both reflect the la- bor market patterns present in the underlying ACS PUMS microdata—for example, construction la- borers are predominantly male, while nursing and teaching occupations show higher proportions of female. (c) shows the distribution of education lev- els across occupations. The distributions reflect the patterns observed in the underlying ACS PUMS microdata. For example, software developers and accountants tend to have higher education levels, while occupations such as construction laborers include more individuals with lower formal educa- tion. (d) shows the relationship between age and predicted years of work experience. As expected, work experience generally increases with age. This pattern indicates that the PSID-based model gen- erates realistic career trajectories that align with typical labor market patterns. Taken together, these statistics confirm thatPopResumepreserves realis- tic demographic and qualification structures while enabling controlled causal analysis. B.4 Example Resume Example resumes from the constructed dataset are shown in Fig. 8. These examples illustrate the input formats used in our evaluation, including text re- sumes for LLM-based scoring and image resumes for VLM-based scoring with and without profile photos. B.5 Validation of Demographic Information in Profile Images To check whether gender and race information were correctly reflected in the generated profile images, we employed CLIP for zero-shot classifi- cation. Specifically, gender was evaluated using two-class zero-shot classification with the prompts “a photo of a man” and “a photo of a woman.” Race was evaluated using the prompts “a photo of a white person,” “a photo of an Asian/Pacific Islander per- son,” and “a photo of a Black person.” Based on this procedure, we confirmed that all generated im- ages are consistent with the information specified in the prompt. For the evaluation of age, we used DeepFace 4 and measured the deviation between the age spec- ified in the prompt and the age predicted by the model. The average deviation was 6.56 years. How- ever, we note that age prediction from facial images is inherently nuanced and subject to considerable uncertainty; therefore, this value should be inter- preted as a rough indicator rather than an exact measure of age consistency. 4 https://github.com/serengil/deepface 13 Accountants Construction Developers Nurses Teachers 0.0 0.2 0.4 0.6 0.8 1.0 Proportion FemaleMale (a) Gender distribution by occupation Accountants Construction Developers Nurses Teachers 0.0 0.2 0.4 0.6 0.8 Proportion Asian/Pacific IslanderBlackWhite (b) Race distribution by occupation Construction Accountants Developers Nurses Teachers 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Proportion High school graduate/GED Some college (no degree) Associate's degree Bachelor's Master's Professional degree Doctorate (c) Education level distribution by occupation 202530354045 Age 0 5 10 15 20 25 Years of experience (d) Age vs experience. (predicted years of experience derived from PSID-based modeling.) Figure 7: Statistical characteristics of the PopResume dataset. 14 MARISA ACOSTA CA, USA | +1-288-906-4731 | marisa.acosta@example.com WORK HISTORY Accountants Auditors, Balanced Books Co., 2020.12–2023.01 -Led cross-functional financial integration -Led accounting process optimization Accountants Auditors, SolidBooks Services, 2018.10–2020.12 -Processed expense reimbursements -Assisted with internal control testing EDUCATION Bachelor's, Accounting, Hillview University, 2017 -Studied principles of accounting policy analysis -Completed coursework in accounting information systems (a) Text resume used for LLM-based scoring. MARISA ACOSTA CA, USA | +1-288-906-4731 | marisa.acosta@example.com WORK HISTORY Accountants Auditors, Balanced Books Co., 2020.12–2023.01 - Led cross-functional financial integration - Led accounting process optimization Accountants Auditors, SolidBooks Services, 2018.10–2020.12 - Processed expense reimbursements - Assisted with internal control testing EDUCATION Bachelor's, Accounting, Hillview University, 2017 • Studied principles of accounting policy analysis • Completed coursework in accounting information systems (b) Image resume without a profile photo used for VLM-based scoring. MARISA ACOSTA CA, USA | +1-288-906-4731 | marisa.acosta@example.com WORK HISTORY Accountants Auditors, Balanced Books Co., 2020.12–2023.01 - Led cross-functional financial integration - Led accounting process optimization Accountants Auditors, SolidBooks Services, 2018.10–2020.12 - Processed expense reimbursements - Assisted with internal control testing EDUCATION Bachelor's, Accounting, Hillview University, 2017 • Studied principles of accounting policy analysis • Completed coursework in accounting information systems (c) Image resume with a profile photo used for VLM-based scoring. Figure 8: Example resumes from the constructed dataset. 15 C Details on Estimation of Path-Specific Effects C.1 Identifiability All terms used for the estimation are identifiable as follows, as proved by Zhang et al. (2025) and Plecko and Bareinboim (2024). E[Y x 0 ] = X z E[Y|x 0 ,z]P(z),(6) E[Y x 1 ] = X z E[Y|x 1 ,z]P(z),(7) E[Y x 1 ,W x 0 ] = X w,z E[Y|x 1 ,w,z]·P(w|x 0 ,z)P(z),(8) E[Y x 1 ,B x 0 ,R x 1 ,R x 1 ] = X b,r,z E[Y|x 1 ,b,r,z]·P(b|x 0 ,r,z)P(r|x 1 ,z)P(z).(9) E[Y x 1 ,B x 1 ,R x 0 ,R x 0 ] = X b,r,z E[Y|x 1 ,b,r,z]·P(b|x 1 ,r,z)P(r|x 0 ,z)P(z).(10) C.2 Parametrization Lemma 2(Parametrization). φ(V;μ,π)≜π 3 Y−μ 3 (B,R,X,Z) +π 2 μ 3 (B,R,x 1 ,Z)−μ 2 (R,X,Z) +π 1 μ 2 (R,x 1 ,Z)−μ 1 (X,Z) +μ 1 (x 0 ,Z). (11) is a valid representation in the sense thatE[φ(V;μ,π)] =Eq.(10). Proof. We note that the proof is largely adopted from Section A.3 in Zhang et al. (2025). Further, we note that theπ 3 (B,R,X,Z) = P(X=x 0 |R,Z) P(X|R,Z) 1[X=x 1 ] P(X=x 0 |Z) can be represented as P(R|X=x 0 ,Z)1[X=x 1 ] P(R|X,Z)P(X|Z) , which follows directly from Bayes’ rule. μ 2 (R,X,Z)≜E[μ 3 (B,R,x 1 ,Z)|R,X,Z] = X b μ 3 (b,R,x 1 ,Z)P(b|R,X,Z) = X b E[Y|b,R,x 1 ,Z]P(b|R,X,Z). (12) and μ 1 (X,Z)≜E[μ 2 (R,x 1 ,Z)|X,Z] = X r μ 2 (r,x 1 ,Z)P(r|X,Z) = X b,r E[Y|b,r,x 1 ,Z]P(b|r,x 1 ,Z)P(r|X,Z). (13) Therefore, E[μ 1 (x 0 ,Z)] =Eq. (10).(14) 16 Furthermore, E[π 3 (B,R,X,Z)Y] =E[π 3 (B,R,X,Z)μ 3 (B,R,X,Z)] = X b,r,x,z μ 3 (b,r,x,z) P(r|x 0 ,z)1[x=x 1 ] P(r|x,z)P(x|z) P(b,r,x,z) = X b,r,z μ 3 (b,r,x 1 ,z)P(b|x 1 ,r,z)P(r|x 0 ,z)P(z) =Eq. (10). (15) Also, E[π 2 (R,X,Z)μ 2 (R,X,Z)] = X r,x,z μ 2 (r,x,z) P(r|x 0 ,z)1[x=x 1 ] P(r|x,z)P(x|z) P(r,x,z) = X r,z μ 2 (r,x 1 ,z)P(r|x 0 ,z)P(z) = X r,z X b E[Y|b,r,x 1 ,z]P(b|r,x 1 ,z)P(r|x 0 ,z)P(z) =Eq. (10). (16) Finally, E[π 1 (X,Z)μ 1 (X,Z)] =E[μ 1 (x 0 ,Z)] =Eq. (10).(17) C.3 Doubly Robustness Property Lemma 3(Doubly Robustness).For any arbitrary ˆ μ, ˆ π, theφin Eq.(11)satisfies E[φ(V;μ,π)]−E[φ(V; ˆ μ, ˆ π)] = Σ 3 i=1 O P (||μ i − ˆ μ i || P ||π i − ˆ π i || P )(18) Proof.Following the Section A.4 in Zhang et al. (2025), it is enough to show that two equalities. One is E[π 3 (B,R,X,Z) ˆ μ 3 (B,R,X,Z)] =E[π 2 (R,X,Z) ˆ μ 2 ∗ (R,X,Z)](19) where ˆ μ 2 ∗ ≜E[ ˆ μ 3 (B,R,x 1 ,Z)|R,X,Z]for any fixed ˆ μ 3 . The equality holds since E[π 2 (R,X,Z) ˆ μ 2 ∗ (R,X,Z)] =E[π 2 (R,X,Z)E[ ˆ μ 3 (B,R,x 1 ,Z)|R,X,Z]] = Σ b,r,x,z ˆ μ 3 (b,r,x 1 ,z)P(b|r,x,z)π 2 (r,x,z)P(r,x,z) = Σ b,r,x,z ˆ μ 3 (b,r,x 1 ,z)P(b|r,x,z) P(r|x 0 ,z)1[x=x 1 ] P(r|x,z)P(x|z) P(r,x,z) = Σ b,r,z ˆ μ 3 (b,r,x 1 ,z)P(b|r,x 1 ,z)P(r|x 0 ,z)p(z) =E[π 3 (B,R,X,Z) ˆ μ 3 (B,R,X,Z)] (20) The other equality is E[π 2 (R,X,Z) ˆ μ 2 (R,X,Z)] =E[π 1 (X,Z) ˆ μ 1 ∗ (X,Z)](21) where ˆ μ 1 ∗ ≜E[ ˆ μ 2 (R,x 1 ,Z)|X,Z]for any fixed ˆ μ 2 . This equality also holds since E[π 1 (X,Z) ˆ μ 1 ∗ (X,Z)] =E[ ˆ μ 1 ∗ (x 0 ,Z)] =E[E[ ˆ μ 2 (R,x 1 ,Z)|x 0 ,Z]] = Σ z Σ r ˆ μ 2 (r,x 1 ,z)P(r|x 0 ,z)P(z) =E[π 2 (R,X,Z) ˆ μ 2 (R,X,Z)] (22) 17 C.4 Grouping of High Cardinality Attributes As outlined in Sec. 3.2, we groupfirst_name,sur- name,state, andedu_levelvariables for the stable estimation of path-specific effects. Forfirst_name, we use statistics from SSA dataset, which provides distributions of names by gender and birth cohort. Based on these statistics, we derive two grouping variables from each first name: agender-typicality category and anage-typicalitycategory. Specifi- cally, names whose probability exceeds a prede- fined threshold of 0.75 are labeled asmale-typical orfemale-typical, while the remaining names are categorized asneutral. Age typicality is deter- mined using the same procedure with a threshold of 0.5, producing three age groups (young,mid, and old) with aneutralcategory for low-confidence cases. For surnames, we use the U.S. Census sur- name dataset, which reports the racial distribution associated with each surname. Using these statis- tics, we assign surnames toAsian,Black, orWhite categories when the corresponding probability ex- ceeds a threshold of 0.5; all others are labeled as neutral. Finally, we group the U.S. states into four major geographic regions (Northeast, Mid- west, South, and West) following the U.S. Census Bureau regional classification (U.S. Census Bureau, 2013). Theedu_levelvariable from (U.S. Census Bureau, 2023) originally contains seven categories. We group them into three levels:low, including High school graduate/GED,Some college;medium, includingAssociate’s degree,Bachelor’s; andhigh, includingMaster’s,Professional degree, andDoc- torate. C.5 Hyperparameter Search For the estimation ofP(X| ·), hyperparameters are selected by minimizing the Brier score, while forE[Y|·], hyperparameters are selected by mini- mizing the mean squared error (MSE). The hyper- parameter search space is defined as follows: •n_estimators:5,10,20,50,100 •max_depth:1,2,3,4,5 •reg_lambda:0.5,1,2,5 D Prompt for Resume Scoring We instruct the model to assign a suitability score to a resume given a job description using the fol- lowing prompt. The model is instructed to return a single numerical score between 0 and 100 in a pre- defined JSON format. For VLM-based evaluation, the same prompt is used, except that the resume is provided as an image instead of text. You are a strict resume screening grader that grades resumes based on job descriptions. Given a JOB DESCRIPTION and a RESUME , provide only a single score from 0 to 100. Rules: - Response format: " score ": X. X - Do NOT provide explanations or extra text. JOB DESCRIPTION: job_text RESUME: resume_text E Additional Experimental Results Tab. 4 and Tab. 5 report the complete experimental results across all configurations and models for the protected attributesgenderandrace, respectively. 18 ModelCaseTENDENIERIEBIE Accountants Auditors LLM Llama-3.1-8b-instructC1,C52.337 [2.162, 2.526]3.365 [2.943, 3.777]-1.028 [-1.452, -0.587]-3.048 [-3.746, -2.384]2.021 [1.374, 2.676] Mistral-7b-instruct-v0.2-2.388 [2.080, 2.697]0.972 [-1.088, 3.044]1.416 [-0.540, 3.404]1.524 [-0.171, 3.321]-0.108 [-0.927, 0.684] GPT-4o-miniC1,C51.179 [1.053, 1.327]2.217 [1.822, 2.589]-1.038 [-1.393, -0.606]-2.627 [-3.129, -2.118]1.589 [1.174, 1.982] Gemini-2.5-Flash-Lite (LLM)C1,C51.076 [0.903, 1.264]5.251 [4.762, 5.713]-4.175 [-4.647, -3.693]-6.207 [-6.844, -5.592]2.032 [1.516, 2.552] VLM (w/o profile image) Qwen2.5-vl-7b-instructC1,C50.255 [0.194, 0.317]0.642 [0.381, 0.907]-0.387 [-0.651, -0.134]-1.462 [-1.728, -1.197]1.074 [0.877, 1.262] InternVL-8bC5-0.022 [-0.047, 0.003]-0.031 [-0.150, 0.086]0.009 [-0.109, 0.125]-0.231 [-0.338, -0.127]0.240 [0.163, 0.312] GPT-4oC1,C50.288 [0.226, 0.357]-1.150 [-1.575, -0.765]1.438 [1.045, 1.860]0.690 [0.244, 1.124]0.748 [0.503, 0.990] Gemini-2.5-Flash-Lite (VLM)-0.494 [0.404, 0.586]0.111 [-0.394, 0.602]0.384 [-0.102, 0.917]0.019 [-0.414, 0.460]0.365 [0.099, 0.630] VLM (w/ profile image) Qwen2.5-vl-7b-instructC50.513 [0.444, 0.582]0.163 [-0.067, 0.390]0.350 [0.118, 0.570]-0.773 [-1.057, -0.493]1.123 [0.919, 1.313] InternVL-8bC10.342 [0.317, 0.367]0.139 [0.040, 0.255]0.203 [0.082, 0.300]-0.039 [-0.148, 0.068]0.242 [0.177, 0.303] GPT-4oC1,C50.391 [0.325, 0.459]0.385 [0.161, 0.593]0.006 [-0.197, 0.218]-0.589 [-0.868, -0.306]0.595 [0.425, 0.792] Gemini-2.5-Flash-Lite (VLM)C1,C50.841 [0.763, 0.928]1.908 [1.501, 2.317]-1.067 [-1.486, -0.669]-1.673 [-2.044, -1.326]0.607 [0.393, 0.805] (Continued — see full caption at Tab. 4) ModelCaseTENDENIERIEBIE Construction Laborers LLM Llama-3.1-8b-instruct--0.275 [-0.611, 0.053]-0.134 [-0.615, 0.405]-0.141 [-0.609, 0.281]0.238 [-0.316, 0.803]-0.379 [-0.759, 0.006] Mistral-7b-instruct-v0.2C1, C20.241 [-0.046, 0.575]-2.429 [-3.264, -1.532]2.669 [1.845, 3.426]2.428 [1.660, 3.298]0.242 [-0.021, 0.480] GPT-4o-miniC5-1.231 [-1.443, -1.021]0.040 [-0.335, 0.413]-1.271 [-1.595, -0.926]-0.676 [-1.028, -0.333]-0.595 [-0.833, -0.345] Gemini-2.5-Flash-Lite (LLM)C1-2.758 [-3.074, -2.456]-1.358 [-1.863, -0.797]-1.400 [-1.882, -0.991]-0.443 [-0.949, 0.003]-0.957 [-1.296, -0.606] VLM (w/o profile image) Qwen2.5-vl-7b-instructC1,C5-1.444 [-1.604, -1.281]-0.316 [-0.607, -0.031]-1.128 [-1.388, -0.873]-0.576 [-0.842, -0.359]-0.552 [-0.714, -0.387] InternVL-8bC10.073 [0.048, 0.099]0.140 [0.060, 0.213]-0.068 [-0.134, 0.002]-0.073 [-0.133, -0.009]0.006 [-0.008, 0.019] GPT-4oC5-0.476 [-0.612, -0.345]0.017 [-0.218, 0.232]-0.493 [-0.700, -0.284]-0.349 [-0.560, -0.134]-0.144 [-0.273, -0.022] Gemini-2.5-Flash-Lite (VLM)C1,C50.559 [0.339, 0.793]2.956 [2.475, 3.472]-2.397 [-2.833, -1.966]-2.662 [-3.105, -2.235]0.265 [0.150, 0.382] VLM (w/ profile image) Qwen2.5-vl-7b-instructC1,C5-1.061 [-1.212, -0.907]-0.861 [-1.136, -0.588]-0.200 [-0.460, 0.044]0.396 [0.115, 0.648]-0.596 [-0.792, -0.410] InternVL-8bC1,C57.164 [6.750, 7.542]7.364 [6.938, 7.784]-0.200 [-0.302, -0.101]-0.389 [-0.501, -0.272]0.189 [0.130, 0.254] GPT-4oC10.598 [0.443, 0.755]1.157 [0.863, 1.443]-0.559 [-0.805, -0.286]-0.424 [-0.677, -0.151]-0.135 [-0.261, 0.000] Gemini-2.5-Flash-Lite (VLM)C10.445 [0.215, 0.679]0.436 [0.066, 0.835]0.009 [-0.310, 0.357]-0.204 [-0.503, 0.102]0.213 [0.118, 0.306] (Continued — see full caption at Tab. 4) 19 ModelCaseTENDENIERIEBIE Elementary Middle School Teachers LLM Llama-3.1-8b-instructC30.975 [0.818, 1.121]0.087 [-0.391, 0.583]0.888 [0.371, 1.349]0.054 [-0.575, 0.674]0.834 [0.394, 1.279] Mistral-7b-instruct-v0.2C11.622 [1.277, 1.976]5.457 [3.044, 7.795]-3.835 [-6.111, -1.453]-4.059 [-6.104, -1.721]0.224 [-0.273, 0.642] GPT-4o-mini--0.172 [-0.274, -0.066]-0.297 [-0.760, 0.152]0.125 [-0.329, 0.612]-0.265 [-0.751, 0.241]0.390 [0.101, 0.692] Gemini-2.5-Flash-Lite (LLM)C1-0.297 [-0.467, -0.130]-1.316 [-2.160, -0.579]1.019 [0.269, 1.942]0.436 [-0.396, 1.344]0.583 [0.128, 1.022] VLM (w/o profile image) Qwen2.5-vl-7b-instructC1-0.274 [-0.332, -0.209]0.738 [0.446, 1.003]-1.012 [-1.280, -0.711]-1.019 [-1.328, -0.692]0.007 [-0.162, 0.190] InternVL-8b--0.068 [-0.089, -0.048]-0.051 [-0.105, 0.002]-0.017 [-0.069, 0.035]-0.000 [-0.078, 0.074]-0.017 [-0.065, 0.042] GPT-4oC1-0.729 [-0.791, -0.666]-0.355 [-0.648, -0.074]-0.374 [-0.663, -0.073]-0.474 [-0.710, -0.224]0.101 [-0.095, 0.282] Gemini-2.5-Flash-Lite (VLM)C1, C20.240 [-0.071, 0.563]2.448 [0.351, 4.477]-2.207 [-4.226, -0.166]-2.137 [-4.248, -0.199]-0.071 [-0.445, 0.330] VLM (w/ profile image) Qwen2.5-vl-7b-instructC50.150 [0.097, 0.209]0.194 [-0.026, 0.433]-0.044 [-0.284, 0.185]-0.288 [-0.563, -0.007]0.244 [0.088, 0.419] InternVL-8bC10.115 [0.096, 0.135]0.100 [0.008, 0.196]0.015 [-0.077, 0.104]0.044 [-0.068, 0.142]-0.029 [-0.080, 0.030] GPT-4oC1-1.155 [-1.208, -1.098]-0.905 [-1.209, -0.606]-0.250 [-0.559, 0.054]-0.340 [-0.635, -0.027]0.090 [-0.003, 0.183] Gemini-2.5-Flash-Lite (VLM)-0.233 [0.158, 0.299]-0.130 [-0.531, 0.288]0.362 [-0.056, 0.766]0.281 [-0.160, 0.682]0.082 [0.023, 0.149] (Continued — see full caption at Tab. 4) ModelCaseTENDENIERIEBIE Registered Nurses LLM Llama-3.1-8b-instructC10.477 [0.299, 0.701]1.476 [0.791, 2.237]-0.999 [-1.764, -0.274]-0.467 [-1.431, 0.482]-0.532 [-1.197, 0.145] Mistral-7b-instruct-v0.2C1,C5-1.645 [-1.952, -1.331]3.420 [1.893, 4.726]-5.064 [-6.351, -3.609]-4.625 [-5.778, -3.234]-0.440 [-0.835, -0.030] GPT-4o-miniC10.230 [0.103, 0.370]1.893 [1.255, 2.576]-1.663 [-2.385, -0.985]-1.249 [-1.961, -0.549]-0.414 [-0.934, 0.090] Gemini-2.5-Flash-Lite (LLM)C10.580 [0.410, 0.770]1.246 [0.374, 2.236]-0.665 [-1.700, 0.197]-0.467 [-1.478, 0.563]-0.198 [-0.878, 0.413] VLM (w/o profile image) Qwen2.5-vl-7b-instructC1, C2-0.040 [-0.098, 0.020]-0.369 [-0.658, -0.108]0.329 [0.048, 0.621]0.197 [-0.112, 0.533]0.131 [-0.071, 0.345] InternVL-8bC1-0.171 [-0.195, -0.148]0.282 [0.138, 0.426]-0.453 [-0.604, -0.305]-0.417 [-0.565, -0.255]-0.037 [-0.098, 0.030] GPT-4o--0.205 [-0.269, -0.142]0.043 [-0.321, 0.433]-0.248 [-0.621, 0.118]-0.268 [-0.694, 0.129]0.019 [-0.142, 0.179] Gemini-2.5-Flash-Lite (VLM)-0.110 [-0.307, 0.499]-0.805 [-3.728, 2.117]0.915 [-2.080, 3.829]0.725 [-2.312, 3.760]0.190 [-0.402, 0.742] VLM (w/ profile image) Qwen2.5-vl-7b-instructC40.288 [0.228, 0.344]-0.137 [-0.329, 0.046]0.424 [0.231, 0.614]0.600 [0.340, 0.873]-0.176 [-0.371, 0.020] InternVL-8bC1-0.567 [-0.596, -0.541]0.198 [0.017, 0.392]-0.765 [-0.956, -0.586]-0.772 [-0.941, -0.606]0.007 [-0.048, 0.055] GPT-4oC1-0.306 [-0.363, -0.248]-0.583 [-0.948, -0.193]0.277 [-0.102, 0.647]0.191 [-0.204, 0.574]0.087 [-0.038, 0.214] Gemini-2.5-Flash-Lite (VLM)C10.446 [0.356, 0.523]0.938 [0.269, 1.653]-0.492 [-1.196, 0.188]-0.423 [-1.087, 0.237]-0.069 [-0.267, 0.173] (Continued — see full caption at Tab. 4) ModelCaseTENDENIERIEBIE Software Developers LLM Llama-3.1-8b-instructC51.192 [1.001, 1.392]-0.113 [-0.428, 0.209]1.305 [0.960, 1.624]-1.456 [-2.069, -0.950]2.761 [2.254, 3.269] Mistral-7b-instruct-v0.2C1,C5-0.226 [-0.363, -0.097]-0.923 [-1.298, -0.600]0.696 [0.407, 1.047]-0.723 [-1.089, -0.331]1.419 [1.163, 1.674] GPT-4o-miniC1, C2, C5-0.034 [-0.166, 0.108]-0.881 [-1.225, -0.547]0.847 [0.524, 1.188]-0.778 [-1.203, -0.319]1.625 [1.283, 1.940] Gemini-2.5-Flash-Lite (LLM)C1,C50.655 [0.455, 0.853]0.474 [0.007, 0.935]0.181 [-0.289, 0.644]-2.529 [-3.216, -1.804]2.710 [2.107, 3.275] VLM (w/o profile image) Qwen2.5-vl-7b-instructC5-0.292 [-0.372, -0.211]-0.151 [-0.325, 0.020]-0.141 [-0.320, 0.045]-0.935 [-1.185, -0.681]0.794 [0.589, 0.991] InternVL-8bC1,C5-0.170 [-0.190, -0.149]-0.220 [-0.281, -0.159]0.050 [-0.010, 0.108]-0.069 [-0.132, -0.004]0.119 [0.087, 0.150] GPT-4oC1, C5-0.356 [-0.419, -0.295]-0.632 [-0.762, -0.511]0.276 [0.149, 0.400]-0.326 [-0.519, -0.136]0.603 [0.454, 0.750] Gemini-2.5-Flash-Lite (VLM)-0.287 [-0.032, 0.581]-0.376 [-1.791, 0.978]0.664 [-0.730, 2.067]0.604 [-0.715, 2.049]0.060 [-0.275, 0.354] VLM (w/ profile image) Qwen2.5-vl-7b-instructC1,C5-0.094 [-0.175, -0.024]-0.321 [-0.492, -0.153]0.228 [0.053, 0.404]-0.610 [-0.830, -0.358]0.838 [0.651, 1.024] InternVL-8bC1,C50.327 [0.304, 0.350]0.399 [0.330, 0.466]-0.072 [-0.142, -0.003]-0.324 [-0.414, -0.239]0.251 [0.203, 0.298] GPT-4oC1-0.193 [-0.278, -0.121]-0.530 [-0.780, -0.278]0.337 [0.105, 0.597]-0.174 [-0.428, 0.107]0.511 [0.367, 0.646] Gemini-2.5-Flash-Lite (VLM)C1,C50.497 [0.419, 0.574]0.689 [0.381, 0.980]-0.192 [-0.477, 0.102]-0.355 [-0.624, -0.055]0.163 [0.120, 0.215] Table 4: Path-specific effects whenx 0 =Female. Bold values indicate confidence intervals excluding zero. The Case column lists matching cases among C1–C5. Rows corresponding only to C1 are shaded gray, while rows containing C2, C3, C4, or C5 use distinct highlight colors. 20 ModelCaseTENDENIERIEBIE Accountants Auditors LLM Llama-3.1-8b-instructC1,C50.658 [0.415, 0.904]-0.417 [-0.743, -0.040]1.075 [0.708, 1.453]-1.349 [-1.802, -0.913]2.424 [2.109, 2.750] Mistral-7b-instruct-v0.2C1-4.267 [-4.637, -3.946]-2.870 [-3.778, -1.972]-1.397 [-2.281, -0.578]-1.409 [-2.209, -0.541]0.012 [-0.289, 0.304] GPT-4o-miniC50.124 [-0.061, 0.293]0.024 [-0.285, 0.303]0.100 [-0.189, 0.408]-1.117 [-1.430, -0.801]1.217 [1.002, 1.421] Gemini-2.5-Flash-Lite (LLM)C1,C51.173 [0.916, 1.392]0.687 [0.393, 0.971]0.486 [0.201, 0.819]-1.382 [-1.711, -1.008]1.868 [1.610, 2.131] VLM (w/o profile image) Qwen2.5-vl-7b-instructC50.202 [0.124, 0.281]0.045 [-0.060, 0.156]0.157 [0.033, 0.264]-0.325 [-0.466, -0.200]0.482 [0.392, 0.575] InternVL-8bC1,C50.117 [0.087, 0.147]0.125 [0.077, 0.175]-0.008 [-0.053, 0.043]-0.057 [-0.111, -0.008]0.049 [0.022, 0.077] GPT-4oC1,C50.311 [0.230, 0.401]0.166 [0.049, 0.283]0.145 [0.009, 0.276]-0.619 [-0.761, -0.469]0.764 [0.668, 0.861] Gemini-2.5-Flash-Lite (VLM)C30.365 [0.256, 0.475]0.033 [-0.181, 0.246]0.332 [0.139, 0.514]0.060 [-0.131, 0.258]0.272 [0.215, 0.332] VLM (w/ profile image) Qwen2.5-vl-7b-instructC1,C5-0.074 [-0.146, 0.004]-0.102 [-0.202, -0.011]0.028 [-0.076, 0.126]-0.495 [-0.612, -0.383]0.524 [0.433, 0.616] InternVL-8bC10.108 [0.081, 0.135]0.090 [0.033, 0.149]0.018 [-0.036, 0.071]-0.010 [-0.069, 0.042]0.028 [0.003, 0.053] GPT-4oC5-0.297 [-0.371, -0.219]0.025 [-0.094, 0.150]-0.322 [-0.446, -0.198]-1.006 [-1.148, -0.879]0.685 [0.586, 0.786] Gemini-2.5-Flash-Lite (VLM)C30.351 [0.246, 0.447]0.119 [-0.078, 0.321]0.232 [0.047, 0.420]-0.075 [-0.265, 0.122]0.307 [0.240, 0.367] (Continued — see full caption at Tab. 5) ModelCaseTENDENIERIEBIE Construction Laborers LLM Llama-3.1-8b-instructC10.815 [0.516, 1.074]0.364 [0.135, 0.620]0.451 [0.278, 0.609]-0.062 [-0.208, 0.077]0.513 [0.372, 0.648] Mistral-7b-instruct-v0.2C5-0.149 [-0.328, 0.037]0.055 [-0.133, 0.240]-0.204 [-0.281, -0.121]-0.325 [-0.406, -0.237]0.121 [0.075, 0.166] GPT-4o-miniC30.350 [0.184, 0.529]0.071 [-0.076, 0.215]0.280 [0.161, 0.393]-0.006 [-0.115, 0.099]0.285 [0.188, 0.381] Gemini-2.5-Flash-Lite (LLM)C50.473 [0.224, 0.703]-0.200 [-0.434, 0.022]0.673 [0.509, 0.840]0.312 [0.144, 0.476]0.361 [0.200, 0.506] VLM (w/o profile image) Qwen2.5-vl-7b-instructC1, C20.022 [-0.069, 0.119]-0.236 [-0.324, -0.139]0.257 [0.189, 0.322]0.023 [-0.044, 0.085]0.234 [0.180, 0.291] InternVL-8bC1,C50.045 [0.026, 0.064]0.032 [0.012, 0.051]0.013 [0.009, 0.018]0.007 [0.002, 0.012]0.006 [0.004, 0.008] GPT-4oC50.223 [0.135, 0.318]-0.009 [-0.106, 0.090]0.232 [0.166, 0.302]0.091 [0.029, 0.154]0.141 [0.088, 0.191] Gemini-2.5-Flash-Lite (VLM)-0.154 [-0.441, 0.765]-0.017 [-0.704, 0.617]0.171 [-0.083, 0.429]0.151 [-0.129, 0.399]0.020 [-0.115, 0.162] VLM (w/ profile image) Qwen2.5-vl-7b-instructC1,C50.209 [0.105, 0.312]-0.158 [-0.248, -0.071]0.368 [0.299, 0.439]0.113 [0.043, 0.180]0.255 [0.191, 0.318] InternVL-8bC1-0.263 [-0.345, -0.172]-0.409 [-0.516, -0.309]0.145 [0.083, 0.212]0.044 [-0.010, 0.098]0.101 [0.060, 0.148] GPT-4oC1,C5-0.385 [-0.484, -0.274]-0.743 [-0.849, -0.634]0.358 [0.277, 0.436]0.183 [0.113, 0.254]0.176 [0.124, 0.223] Gemini-2.5-Flash-Lite (VLM)C1,C5-0.151 [-0.311, -0.011]-0.346 [-0.510, -0.203]0.194 [0.127, 0.266]0.255 [0.183, 0.332]-0.060 [-0.093, -0.024] (Continued — see full caption at Tab. 5) ModelCaseTENDENIERIEBIE Elementary Middle School Teachers LLM Llama-3.1-8b-instructC1,C50.382 [0.170, 0.563]0.266 [0.145, 0.390]0.117 [-0.066, 0.306]-0.162 [-0.323, -0.004]0.279 [0.095, 0.477] Mistral-7b-instruct-v0.2C1,C5-2.813 [-3.242, -2.347]-2.086 [-2.612, -1.541]-0.727 [-1.066, -0.345]-1.006 [-1.362, -0.604]0.279 [0.113, 0.439] GPT-4o-miniC10.233 [0.101, 0.367]0.124 [0.022, 0.229]0.109 [-0.028, 0.249]-0.073 [-0.194, 0.053]0.182 [0.053, 0.305] Gemini-2.5-Flash-Lite (LLM)C1,C50.179 [-0.026, 0.391]0.275 [0.095, 0.436]-0.096 [-0.319, 0.103]-0.292 [-0.482, -0.092]0.196 [0.005, 0.392] VLM (w/o profile image) Qwen2.5-vl-7b-instructC10.131 [0.056, 0.211]0.066 [0.016, 0.127]0.065 [-0.003, 0.135]-0.032 [-0.101, 0.034]0.097 [0.027, 0.164] InternVL-8bC1-0.055 [-0.079, -0.030]-0.085 [-0.108, -0.063]0.030 [0.007, 0.054]-0.008 [-0.031, 0.017]0.038 [0.016, 0.060] GPT-4oC1,C50.063 [-0.005, 0.129]0.083 [0.016, 0.158]-0.020 [-0.086, 0.042]-0.110 [-0.173, -0.047]0.090 [0.034, 0.146] Gemini-2.5-Flash-Lite (VLM)--0.222 [-0.576, 0.137]-0.360 [-0.814, 0.121]0.138 [-0.201, 0.496]0.097 [-0.263, 0.449]0.041 [-0.115, 0.202] VLM (w/ profile image) Qwen2.5-vl-7b-instructC1,C5-0.051 [-0.118, 0.018]-0.069 [-0.118, -0.017]0.018 [-0.050, 0.092]-0.104 [-0.164, -0.045]0.122 [0.052, 0.190] InternVL-8bC1,C5-0.331 [-0.357, -0.306]-0.415 [-0.442, -0.390]0.084 [0.058, 0.109]0.041 [0.016, 0.066]0.043 [0.021, 0.063] GPT-4oC1,C5-0.500 [-0.566, -0.435]-0.402 [-0.467, -0.338]-0.098 [-0.156, -0.042]-0.179 [-0.236, -0.127]0.081 [0.034, 0.130] Gemini-2.5-Flash-Lite (VLM)C40.105 [0.019, 0.192]0.019 [-0.098, 0.142]0.087 [0.008, 0.166]0.095 [0.009, 0.175]-0.008 [-0.038, 0.020] (Continued — see full caption at Tab. 5) 21 ModelCaseTENDENIERIEBIE Registered Nurses LLM Llama-3.1-8b-instructC1, C20.081 [-0.071, 0.236]-0.387 [-0.548, -0.210]0.468 [0.270, 0.672]0.352 [0.112, 0.597]0.116 [-0.106, 0.338] Mistral-7b-instruct-v0.2C1,C5-3.120 [-3.362, -2.896]-1.844 [-2.289, -1.410]-1.276 [-1.727, -0.879]-1.049 [-1.538, -0.623]-0.226 [-0.330, -0.127] GPT-4o-miniC1-0.187 [-0.294, -0.072]-0.309 [-0.435, -0.189]0.122 [-0.023, 0.258]0.099 [-0.062, 0.265]0.023 [-0.113, 0.168] Gemini-2.5-Flash-Lite (LLM)C40.195 [0.045, 0.340]-0.082 [-0.269, 0.112]0.276 [0.078, 0.481]0.256 [0.016, 0.481]0.020 [-0.175, 0.215] VLM (w/o profile image) Qwen2.5-vl-7b-instructC10.068 [0.016, 0.123]0.063 [0.000, 0.125]0.004 [-0.060, 0.076]0.010 [-0.060, 0.079]-0.006 [-0.066, 0.056] InternVL-8bC30.074 [0.056, 0.093]0.025 [-0.003, 0.054]0.049 [0.022, 0.075]-0.005 [-0.035, 0.023]0.054 [0.036, 0.074] GPT-4oC3-0.073 [-0.121, -0.020]0.009 [-0.074, 0.104]-0.082 [-0.169, -0.000]0.011 [-0.081, 0.095]-0.093 [-0.158, -0.031] Gemini-2.5-Flash-Lite (VLM)--0.120 [-0.408, 0.182]-0.252 [-0.797, 0.289]0.132 [-0.297, 0.602]0.090 [-0.366, 0.551]0.042 [-0.168, 0.256] VLM (w/ profile image) Qwen2.5-vl-7b-instruct--0.028 [-0.074, 0.022]-0.032 [-0.075, 0.015]0.004 [-0.051, 0.059]-0.066 [-0.132, 0.005]0.070 [0.011, 0.132] InternVL-8bC1,C5-0.218 [-0.238, -0.196]-0.303 [-0.343, -0.266]0.085 [0.052, 0.119]0.049 [0.017, 0.081]0.037 [0.022, 0.050] GPT-4oC1-0.516 [-0.569, -0.467]-0.430 [-0.504, -0.365]-0.086 [-0.156, -0.018]-0.008 [-0.077, 0.064]-0.078 [-0.124, -0.029] Gemini-2.5-Flash-Lite (VLM)C40.217 [0.139, 0.294]0.066 [-0.068, 0.186]0.151 [0.054, 0.254]0.123 [0.027, 0.224]0.029 [-0.014, 0.073] (Continued — see full caption at Tab. 5) ModelCaseTENDENIERIEBIE Software Developers LLM Llama-3.1-8b-instructC1-0.412 [-0.553, -0.268]0.552 [0.285, 0.783]-0.964 [-1.226, -0.688]-0.261 [-0.619, 0.070]-0.703 [-0.994, -0.413] Mistral-7b-instruct-v0.2C1-0.825 [-0.937, -0.722]-0.656 [-1.432, -0.015]-0.169 [-0.826, 0.583]-0.423 [-0.887, 0.061]0.254 [-0.021, 0.558] GPT-4o-miniC1-0.527 [-0.647, -0.411]-0.401 [-0.679, -0.134]-0.127 [-0.389, 0.150]-0.145 [-0.459, 0.141]0.018 [-0.175, 0.218] Gemini-2.5-Flash-Lite (LLM)C4-0.767 [-0.930, -0.615]-0.064 [-0.339, 0.232]-0.703 [-1.002, -0.392]-0.753 [-1.108, -0.403]0.050 [-0.237, 0.342] VLM (w/o profile image) Qwen2.5-vl-7b-instructC5-0.795 [-0.852, -0.737]0.099 [-0.032, 0.223]-0.894 [-1.027, -0.763]-0.302 [-0.436, -0.166]-0.593 [-0.674, -0.509] InternVL-8bC1-0.085 [-0.098, -0.072]0.061 [0.026, 0.100]-0.146 [-0.186, -0.109]-0.019 [-0.056, 0.021]-0.128 [-0.143, -0.111] GPT-4oC4-0.225 [-0.282, -0.176]0.040 [-0.057, 0.138]-0.265 [-0.365, -0.157]-0.194 [-0.309, -0.075]-0.071 [-0.142, 0.004] Gemini-2.5-Flash-Lite (VLM)-0.059 [-0.164, 0.312]0.672 [-0.233, 1.640]-0.613 [-1.548, 0.284]-0.591 [-1.498, 0.305]-0.022 [-0.213, 0.166] VLM (w/ profile image) Qwen2.5-vl-7b-instructC1-1.077 [-1.128, -1.027]-0.504 [-0.629, -0.380]-0.574 [-0.705, -0.440]0.062 [-0.089, 0.193]-0.635 [-0.711, -0.560] InternVL-8bC1-0.262 [-0.280, -0.246]-0.165 [-0.204, -0.124]-0.096 [-0.137, -0.060]0.003 [-0.041, 0.046]-0.100 [-0.120, -0.079] GPT-4oC1-0.627 [-0.687, -0.564]-0.918 [-1.246, -0.610]0.290 [-0.034, 0.622]0.252 [-0.011, 0.493]0.039 [-0.049, 0.137] Gemini-2.5-Flash-Lite (VLM)C1,C5-0.120 [-0.174, -0.066]1.230 [1.022, 1.459]-1.350 [-1.568, -1.144]-1.178 [-1.384, -0.980]-0.171 [-0.210, -0.128] Table 5: Path-specific effects for race experiments (x 0 =Non-White). Bold values indicate confidence intervals excluding zero. The Case column lists matching cases among C1–C5. Rows corresponding only to C1 are shaded gray, while rows containing C2, C3, C4, or C5 use distinct highlight colors. 22