Staying Ahead: A Critical Assessment of the GenAI-Assisted Hiring Process Using the OECD AI Principles

Abstract

This critical reflection examines the growing use of GenAI (Generative AI) in hiring in the hospitality and tourism (H&T) industry. Distinct from systematic reviews, this paper synthesizes academic literature, white papers, and the authors’ field-based research experience to explore four topics. First, it assesses how GenAI transforms the six hiring phases from the perspectives of employers and job seekers. Second, ethical risks related to fairness, transparency, data protection, and accountability are critically evaluated using the Organization for Economic Cooperation and Development’s (OECD) AI Principles for “trustworthy AI,” which informs the development of six propositions. Third, practical recommendations are presented to help H&T organizations and job seekers remain competitive, relevant, and compliant with the OECD AI Principles as they navigate the still-evolving AI landscape. Lastly, research questions and methodologies are proposed for future investigations. This work contributes to the ongoing interdisciplinary dialogue and supports the responsible adoption of AI in hiring.

Keywords

artificial intelligence generative AI selection human resource management information technology critical reflection

Highlights

This study demonstrates that GenAI has transformed the six hiring phases.

AI hiring is assessed with the Organization for Economic Cooperation and Development’s AI Principles.

Actionable recommendations are provided for organizations and job seekers.

A research agenda and methodological roadmap are proposed for scholars.

Introduction

ChatGPT and similar GenAI applications have rapidly become the fastest-growing consumer application in the history of the hospitality and tourism (H&T) industry (Gursoy et al., 2023). Today, GenAI systems are generally perceived as a major disruptive technology poised to exert broad and unprecedented impacts on society, the business world, and the H&T sector in particular (e.g., Amankwah-Amoah et al., 2024; Dogru et al., 2024; Garcia & Kwok, 2025). GenAI can enhance employee productivity, refine consumer experiences, foster innovations (e.g., Kim et al., 2025; H. Li et al., 2025), and have a direct impact on the H&T industry by automating mechanical and repetitive tasks, hence addressing persistent labor shortage challenges (Dogru et al., 2025; Dwivedi et al., 2024). Many H&T firms are rapidly integrating GenAI into their HR processes to achieve higher efficiency, including how they attract, assess, and hire talent (Garcia & Kwok, 2025). Nevertheless, the widespread adoption of GenAI in HR practices has also raised urgent ethical concerns such as fairness, transparency, and accountability (Andrieux et al., 2024; Budhwar et al., 2023), particularly in the H&T industry, which has a large number of small- and medium-sized enterprises (SMEs), and employs many underrepresented workers. As the H&T industry is at a critical crossroads as GenAI begins to reshape the traditional hiring practices, we adopt a reflective approach to analyze its growing influence on H&T firms’ hiring process, using the Organization for Economic Cooperation and Development’s (OECD) AI Principles as an evaluative framework. Our objective is to offer timely insight, ethical considerations, and practical guidance for H&T’s two primary stakeholders: employers, including their hiring and HR managers, and job seekers, including college students.

The H&T industry has long struggled with a labor shortage challenge (Kwok, 2022), a staffing constraint further intensified by recent Quiet Quitting and Great Resignation trends (Hamouche et al., 2023; Liu-Lastres et al., 2023, 2024). Leveraging technologies to attract, select, and develop top talent has long been recognized as an organization’s essential competitive advantage (S. L. Thomas & Ray, 2000). Thus, gaining a deeper understanding of how GenAI transforms hiring practices can inform H&T firms about strategies for effective talent acquisition. Beyond the specific H&T content, a recent Deloitte survey of 2,770 director- or C-suite level leaders from six industries in 14 countries between May and June 2024 reveals that embedding GenAI into functions and processes and using GenAI tools for talent acquisition were the two most desired areas (ranked No. 1 and No. 6 on the list, respectively) where GenAI could deliver the most value (Rowan et al., 2024). Likewise, according to another McKinsey Global Survey of 1,363 working professionals (Singla et al., 2024), 50% of respondents reported that their companies were able to implement GenAI in HR functions within 4 months (with 16% indicating less than 1 month, and 34% between 1–4 months) and witnessed meaningful cost reductions. Because GenAI has a high potential for building upon existing automated IT infrastructure to accelerate its implications, AI-empowered HR tools, especially those in the hiring process, have become the fastest-growing segment of corporate AI investment (Yam & Skorburg, 2021). The rapid and widespread adoption of GenAI in H&T firms’ hiring practices will inevitably affect the two primary stakeholders directly involved in the process: organizations and their HR managers who use GenAI tools in hiring, as well as job candidates—including college students—who navigate the AI-mediated hiring process. Targeting these two primary H&T stakeholders who are actively and directly involved in the GenAI-assisted hiring process, our first objective is to discuss:

RQ1: What revolutionary changes does GenAI bring into the hiring process?

This question explores the practical disruptions and innovations GenAI introduces to H&T firms’ hiring process. Meanwhile, companies across sectors, including the H&T industry, have long invested heavily in human resource information systems (HRIS) to automate decision-making in hiring. HRIS assists HR managers in preselecting candidates through basic queries (e.g., matching candidates’ skillsets with job requirements) as well as more sophisticated searches to identify individuals who are likely to fit well within a team (e.g., Malinowski et al., 2008). GenAI operates on significantly more advanced and adaptive IT infrastructures than the earlier automated models in HRIS, which have dramatically enhanced its dynamic capabilities and ability to emulate human-like intelligence and support more nuanced, context-sensitive decision-making processes (Amankwah-Amoah et al., 2024; Dogru et al., 2024). The rapid evolution of AI technologies has profoundly transformed our society and the global economy, a paradigm shift that calls for interdisciplinary efforts and international collaboration to carefully examine the broader consequences of fairness, transparency, decision quality, and other critical ethical concerns (Yeung, 2020).

As the first global and intergovernmental initiative on AI, the OECD introduced the AI Principles at the 2019 G20 (Group of Twenty that represents the world’s largest economies) Osaka Summit to promote responsible stewardship of “trustworthy AI.” These guidelines were subsequently updated in November 2023 and May 2024 in response to the widespread implementation of GenAI since 2019 (OECD, 2025). The OECD AI Principles emphasize five core values: that AI should (1) promote social well-being and sustainability growth; (2) respect human rights, fairness, and privacy; (3) be understandable and transparent; (4) function reliably and safely; and (5) hold developers and users accountable for outcomes (Nguyen et al., 2023; OECD, 2025). These OECD AI Principles guide us in a critical assessment of GenAI's impact on hiring, answering:

RQ2: What ethical concerns arise when aligning GenAI hiring practices with the OECD AI Principles?

This question guides us to perform a critical assessment of GenAI’s impact on hiring that was discussed in RQ1 through an international governance lens, guiding us to advance specific practical and research recommendations to promote the ethical use of trustworthy AI in hiring, which will be further discussed in RQ3 and RQ4. Inevitably, the extensive adoption of GenAI in H&T firms’ hiring practices is poised to significantly affect H&T firms, from large corporations to SMEs, their HR managers, and job seekers who increasingly rely on various GenAI tools in the hiring process. In this reflection, we also aim to initiate a discussion of how these two primary stakeholders can proactively respond to these revolutionary shifts—responses that may carry broad practical implications extending beyond the H&T discipline.

RQ3: How can the two H&T stakeholders—H&T firms and their HR managers—as well as job seekers remain competitive, relevant, and compliant with the OECD AI Principles amid these revolutionary changes?

This question connects our critical analysis to actionable strategies for navigating GenAI adoption in hiring. Within the H&T literature, much of the existing AI research has centered around customer interface or employees’ perceptions of AI, and its effects on HR functions have been under-explored (Gursoy & Cai, 2025; Kim et al., 2025; Kong et al., 2023). Specific areas where GenAI is projected to have immediate, meaningful, cost-saving benefits, such as various HR processes suggested in the industry reports (Rowan et al., 2024; Singla et al., 2024), now warrant research attention. In particular, because HR is an essential business function across both for-profit and non-profit sectors, exploring GenAI's role in hiring has far-reaching societal and economic implications beyond the H&T field. In this research, we also aim to derive novel research ideas with corresponding questions from our synthesis of responses to RQ1 and RQ2. We aim to stimulate further empirical investigations into under-researched areas that continue to challenge many H&T scholars by presenting a series of research questions and suggestions on relevant and appropriate methodologies. We thus address:

RQ4: What new research ideas emerge because of the updates in the GenAI-assisted hiring process?

This final question points out specific research directions for cross-disciplinary inquiries. To answer the above four research questions, we adopt a reflective approach grounded in the authors’ critical assessment of a timely topic: GenAI in hiring. Typically, this approach differs considerably from an empirical study that supports the conclusions with data or a traditional narrative/systematic literature review paper, which is commonly designed to pinpoint knowledge gaps and propose theoretical advancements through methods such as bibliometric, content, or meta-analytical reviews. Similar to other critical reflection or foresight articles published in leading H&T and organizational behavior (OB) journals (e.g., Andrieux et al., 2024; Black & van Esch, 2020; Cha et al., 2025; Kwok, 2022; Neely et al., 2023; Yang & Wang, 2025), the primary purpose of this work is to spark intellectual dialogues on a contemporary issue warranting immediate research attention. Critical reflection and foresight articles can also significantly contribute to work and organizational psychology for their ability to connect concealed interests and ideologies with managerial thoughts and actions (Reynolds, 1998; Seubert et al., 2023). Reflecting on our industry, academic, and advising experiences regarding GenAI’s impact on the hiring process, we synthesize our analysis of the purposefully selected relevant journal articles and industry reports to answer the four research questions presented in the subsequent sections. We conclude our discussion in the last section with a closing remark, limitations, and directions for future research. Although this paper is not an exhaustive or systematic review and presents only preliminary ideas, we initiate an ongoing scholarly dialogue to encourage and possibly inspire further empirical studies to advance knowledge in the still-evolving GenAI-assisted hiring process.

Generative AI’s Revolutionary Changes to the Hiring Process

In this section, we begin by outlining a typical hiring process in an H&T organization to answer RQ1. Reflecting on our field and research expertise in recruitment and selection, we break down the hiring process into six distinct phases: (1) accepting job applications and resumes, (2) screening application materials, (3) conducting and rating job interviews, (4) administering and evaluating assessment tests, (5) performing background checks, and (6) extending a job offer. We present a visual diagram in Figure 1 to illustrate the sequential flow from one phase to the next and how HR managers reach a final hiring decision for the last few candidates from a large pool of job applicants. Prior to the recent rise of GenAI, many organizations had already digitalized their hiring process in HRIS, laying the foundational IT infrastructure necessary to enable seamless GenAI integration. This helps explain why many companies see immediate opportunities for implementing GenAI tools in the hiring process (Singla et al., 2024). The integration of GenAI revolutionizes the hiring ecosystem, enhancing efficiency, promoting objectivity, and enabling a more personalized candidate experience (Budhwar et al., 2023). We must acknowledge that organizations and individual users may not necessarily choose to adopt GenAI applications in every phase of the hiring process, especially those with limited resources or budgets for implementation and training (e.g., SMEs or job seekers from lower social classes). However, the six phases illustrated in Figure 1 serve as a useful framework for analyzing GenAI’s transformative role in hiring. In particular, we use this framework to examine GenAI-enabled interventions at each phase from the perspectives of two key H&T stakeholders: organizations and their HR managers who perform hiring, as well as job seekers, including college students, who go through the process as candidates.

Phase 1: Accept Job Applications and Resumes

GenAI can significantly enhance efficiency for both employers and job seekers in this phase. Today, Hilton and Hyatt have already used AI-powered portals to streamline the job application process on their recruitment websites (Hyatt, n.d.; Meister, 2018). More H&T companies have also adopted AI-powered chatbots and virtual assistants to assist candidates in the application process. These chatbots can help job seekers fill out the application, address their inquiries in real-time around the clock, and collect relevant information through targeted questions (Black & van Esch, 2020). Notably, many large H&T companies have embraced GenAI usage in this phase. For example, McDonald’s utilizes an AI chatbot called “Olivia” to support the application process 24/7 in the global market (Greenberg, 2025). Chipotle integrates “Ava Cado” conversational AI to help job applicants fill out applications (I. Thomas, 2025). Marriott also uses a career chatbot to create a more personalized application experience for job seekers (Meister, 2018). Hilton uses AI-powered tools to enhance both efficiency and candidates’ experience (Meister, 2018). Businesses can also use GenAI to automatically monitor, report, and defend against cyberattacks, protecting job candidates’ private information in HRIS.

Job seekers benefit from GenAI-enhanced web portals for a smoother application process. Both Marriott and Hilton have observed improved candidate experience after they leveraged AI to make deeper connections with their job candidates (Meister, 2018). Moreover, GenAI can empower them to quickly identify vacant positions with job descriptions that match their skill sets and experiences. For example, platforms such as Talentprice generate matching positions for job seekers based on their preferences and qualifications. Then, GenAI can help them create or fine-tune their application letters and resumes, tailoring them to a position’s job specifications and job descriptions and automating the application process (e.g., filling out forms by pulling the information from resumes). Once applications are submitted, GenAI-enabled chatbots can continue to offer real-time support and respond to follow-up queries. Since Chipotle introduced Ava Cade, the restaurant chain has reported a significant reduction in the average time for completing a job application (down to 8 minutes) and a dramatic increase in completion rate (at 85%; I. Thomas, 2025).

Phase 2: Screen Applications and Resumes

GenAI's NLP (natural language processing) feature can help hiring managers to streamline the screening process by extracting candidates' skills, qualifications, and relevant work experiences from their resumes and application forms (Rane, 2024). GenAI enables recruiters to quickly identify matches between vacant positions and candidates, significantly reducing time and manual work spent in the process (L. Li et al., 2021). For example, Hilton has leveraged AI to automate the candidates’ screening, sourcing, and interviewing process (Meister, 2018). Many restaurant chains, including Chipotle, Darden, Peet’s Coffee, Taco Bell, and Dunkin’, have also used Paradox, an AI-powered conversational hiring platform, to perform some initial screening tasks and help candidates schedule a job interview, in some cases, cutting the hiring process from 10 days to 36 hours (National Restaurant Association, 2023). Furthermore, GenAI can help hiring managers build "ideal" candidate profiles within the organization through an analysis of the attributes possessed by their own successful employees (Andrieux et al., 2024). By comparing candidates' attributes and qualifications against those from the "ideal" profiles, GenAI can predict candidates’ likelihood of success in the position.

GenAI can assist job seekers with language, grammatical, and formatting improvements to customize document content and tailor information that specifically resonates with employers, thereby improving the likelihood of receiving an interview invitation. Wiles et al. (2023) at MIT conducted a study with over 480,000 job seekers from multiple countries. They found that job seekers receiving basic algorithmic assistance for spelling or grammatical errors, word usage, overly used phrases, tone, style, and punctuation (the treatment group) signed 7.8% more contracts than the non-treatment group in their first month of job search. Moreover, when job candidates received a job offer, the treatment group saw an average of 10% higher wages than the non-treatment group. In fact, beyond grammar and error checks, GenAI can help job candidates to fine-tune their resumes and cover letters by using selected keywords that align with job specifications and descriptions.

Phase 3: Conduct and Rate Job Interview(s)

After screening all candidates’ applications and resumes, companies usually invite qualified candidates for interviews. Interactive interviews with GenAI are expected to be the new “norm” in 2025 across all sectors (including restaurants), with GenAI managing schedules, generating job descriptions and relevant interview questions, conducting interactive interviews, and evaluating candidates’ recorded responses to interview questions (Wu, 2024). Chipotle’s chatbot “Ava Cado,” for example, can manage interview schedules for its managers and answer job candidates’ questions (I. Thomas, 2025). Hilton used HireVue, an AI-powered software company, to schedule, conduct, and analyze candidates’ recorded video interviews, reducing its hiring time from 42 days to 5 days (McLaren, 2018). Overall, GenAI can not only create tailored interview questions and assessments based on job requirements but also identify key qualifications and potential red flags of job applicants. Then, AI-driven Chatbots can conduct virtual job interviews by asking candidates role-specific, behavioral, or competency-based questions (Stone et al., 2024). Finally, GenAI can evaluate interviewees’ responses, analyze candidates’ facial expressions, tone, and word choice in job interviews, and score overall fit for the position (Stone et al., 2024).

GenAI-powered interview tools can also be a game-changer for job seekers. Just as GenAI can assist companies and HR managers in this area, it can support job seekers by reviewing their resumes and job descriptions to generate relevant interview questions for practice. GenAI can simulate a hiring manager, enabling job candidates to engage in mock interviews and receive feedback. However, ethical concerns arise when candidates use ChatGPT to generate answers to the interview questions during asynchronous or synchronous interviews, allowing them to read AI-generated answers in interviews to increase their chances of receiving a job offer (Canagasuriam & Lukacik, 2025). Hence, we anticipate that job candidates must prepare for interviews with both GenAI and real managers during the hiring process.

Phase 4: Administer and Evaluate Assessment Tests if Applicable

After interviews, companies may require candidates to complete assessment tests before moving them to the next phase of the hiring process. Companies can use GenAI to build customized assessments to test job candidates’ ability to determine whether they can perform the job functions. HireVue, a firm providing GenAI solutions to companies (e.g., Amazon, Carnival Cruise, and Microsoft) in job interviews and assessments, has already used interactive games to evaluate candidates’ problem-solving skills, learning agility, quantitative aptitude, and other desirable qualifications set by the employers (Yam & Skorburg, 2021). Particularly, for customer service roles, Hilton’s hiring managers now use AI-powered simulations in assessment tests to gauge how a job candidate would react to angry customers (McLaren, 2018). Hyatt uses another AI-driven assessment tool called Pymetrics, where candidates play games that measure cognitive and emotional traits, to identify candidates with traits or profiles similar to those of their top performers (Hyatt, n.d.). Candidates may find GenAI more helpful in knowledge-based assessments than in tests about their behaviors and attitudes. GenAI can likely answer almost any question. Nevertheless, using GenAI to answer questions in assessment tests will create ethical concerns about integrity. Canagasuriam and Lukacik (2025) found that hiring managers rated job candidates higher in performance but lower in honesty measures when evaluating answers generated by ChatGPT (vs. those not generated by ChatGPT). Accordingly, job seekers must clearly understand the boundary between honesty and the pursuit of desirable scores in assessment tests.

Phase 5: Perform Background Checks

Before making a job offer, companies typically conduct background checks on a job candidate after the individual successfully passes all necessary interviews and assessments. GenAI has high potential to provide supplementary, descriptive information for managerial candidates. Before GenAI, many H&T hiring managers had already admitted that they checked on job candidates’ social media and LinkedIn profiles during the hiring process (Kwok & Muñiz, 2021). Managerial candidates’ profile information on social media websites can feed GenAI’s search results. In today’s digital age, executives and senior managers are like public figures. Even if they choose not to use any social media platforms, every time they attend a corporate event, give a speech, or appear in a press-release article, they leave a digital footprint, which can further support GenAI’s search results. Simple prompts like “What do people say about so-and-so at XYZ company?” or “What leadership style does so-and-so at XYZ company have?” on any GenAI platform can generate a plausible candidate narrative. This approach, however, may not work for candidates applying for frontline or entry-level positions, who usually do not have a public social media account and have limited digital presence, creating difficulties for GenAI to identify the collective information about the right person to generate narrative answers to the prompts.

The good news is that standing out in this phase can be within the grasp of all job candidates, even those with little or no leadership experience. Candidates can purposefully create relevant content and tag their affiliations on social media websites to highlight their work and qualifications, helping them to establish a personal brand in the cyber marketplace. This content can become an information source in GenAI’s responses to a query about them. Recent research confirms that hiring managers, including those in the H&T industry, find candidates’ social media content useful for making hiring decisions and regularly use it (Hartwell & Campion, 2020; Kwok & Muñiz, 2021). In another field study about a candidate’s LinkedIn profiles for H&T jobs, Garcia et al. (2023) found that HR managers’ intentions to hire a candidate will become stronger if the candidate has a complete LinkedIn profile with few or no errors, shows relevant work experience, and reveals information consistent with what they conveyed in the interviews. Today, candidates can use new AI features available on LinkedIn to build a more appealing profile. It can be promising for all job candidates, even college students in H&T with little work or leadership experience, to build a reputable personal brand on social media platforms for GenAI background checks.

Phase 6: Extend a Job Offer

Hiring managers will prepare contracts and extend job offers in the last phase. Research has shown that pay transparency laws that require employers to disclose a pay range for openings can significantly reduce the gender wage gap by approximately 20%–40% (Baker et al., 2023). GenAI can help companies generate and review the terms in the contract and perform some preliminary analysis to help determine competitive wages, especially in areas with pay transparency laws (e.g., Ontario, Canada, and California, USA). Additionally, GenAI can help HR managers summarize labor trends and suggest a compensation and benefits package for new hires. As far as legal issues are concerned, job candidates can find even more advantages in the revolutionary changes accompanying GenAI because it will most likely replace lawyers in contract analysis (Armour & Sako, 2020). Before GenAI, few job seekers could afford to hire a legal consultant or HR specialist to review their labor contracts. GenAI can offer advice to job candidates on numerous employment considerations, such as comparing the compensation package and benefits against industry standards and market rates, being informed of fair working conditions, identifying alternative separation clauses, ensuring labor law compliance, and understanding workforce and employment trends. At the same time, GenAI has high potential to assist job candidates in labor contract negotiations for higher wages and better work conditions with rich market insights.

In summary, GenAI's revolutionary impact on hiring is evident. H&T companies are increasingly integrating GenAI across multiple phases in the process, especially in the earlier phases, such as accepting and pre-screening applications. Referring to our earlier discussion on Chipotle, the restaurant chain has explicitly chosen not to use GenAI for resume screening while emphasizing its prioritization of human involvement in hiring decisions (I. Thomas, 2025). This case helps explain why fewer specific H&T examples exist in the later hiring phases or stages closer to final hiring decisions, where organizations may rely more heavily on human judgment. Additionally, most documented cases come from large H&T corporations, such as Hilton, Marriott, Hyatt, McDonald’s, and Carnival Cruise Lines. However, mainstream media has paid less attention to how H&T SMEs might adopt GenAI, suggesting a digital divide where larger firms may have an advantage in navigating and implementing new technologies. As a summary of our discussion to answer RQ1, we created Figure 2 to illustrate how GenAI has changed each hiring phase for the two primary H&T stakeholders in the process: employers and job seekers.

A Critical Assessment of Genai in Hiring: Ethical Implications and Alignment With OECD AI Principles

To answer RQ2, we critically assess GenAI's impact on hiring in line with the OECD AI Principles across the six hiring phases. The OECD AI Principles include growth & well-being (GW), fairness & diversity (FD), transparency & explainability (TE), robustness, security, and privacy protections (RSPP), and accountability (Acc). At the end of this section, we present our conclusions with six propositions based on our answers to RQ1 and RQ2. These conclusions also inform us to advance specific practical implications to answer RQ3, and to identify areas that need research attention to answer RQ4.

Phase 1: Accept Job Applications and Resumes

GenAI has enabled companies to streamline and personalize this phase by powering dynamic portals, tailoring job descriptions, and assisting job candidates with chatbots. The increased efficiencies align with the GW principle because GenAI can help H&T companies like Hilton and Marriott improve candidate experience (Meister, 2018) and Chipotle to substantially increase the completion rate (I. Thomas, 2025). Meanwhile, GenAI has the potential to help HR managers improve the clarity and inclusivity in job descriptions, as in the case of Hyatt Hotels (Hyatt, n.d.), ultimately lowering or reducing the entry barriers for underrepresented groups, meeting the FD principle. Nevertheless, recent studies have shown that job postings generated by large language models (LLMs) can embed biased language, disadvantaging underrepresented job seekers (An et al., 2024). Furthermore, recent literature also shows that the automated portals could raise issues under TE principles because candidates do not always understand how their data is collected or ranked and may not even realize they are interacting with an AI assistant during the job application process (Xiong & Kim, 2025). Companies that do not voluntarily release detailed algorithms showing how job postings are tailored will create a black-box hiring process, lacking explainability, and hence violating the TE principle. In another example, McDonald's was reported to expose millions of job applicants’ personal data after security researchers conducted a test with the restaurant chain's McHire website, an AI-powered recruitment platform (Bradley, 2025), showing that companies could easily violate the RSPP and Acc principles if they do not take their security measures seriously.

Phase 2: Screen Applications and Resumes

On the one hand, GenAI enables faster hiring decisions for large candidate pools by automatically matching candidates’ key information extracted from their application materials with that of job openings (L. Li et al., 2021; Rane, 2024). The National Restaurant Association (2023) reported that several of its large corporate members had seen a dramatic reduction in hiring time due to AI’s power in pre-screening (from 10 days to 36 hours). These practices improve efficiency and align with the GW principle. On the other hand, concerns about non-discrimination and fairness arise because GenAI models may inadvertently reflect biases in the training data (Andrieux et al., 2024). Armstrong et al. (2024) found that LLMs rated resumes with White male names higher in more White-dominated occupations despite having qualifications equal to others’. In another study with over 140,000 individuals worldwide, Otis et al. (2024) reported that women across regions, sectors, and occupations were less likely to use GenAI tools than men. This gender gap will further push women into a “marginal” group as they become less knowledgeable, familiar, or confident with the available GenAI tools. These potential biases in race and gender violate the FD principle. When organizations fail to provide sufficient explanations of how GenAI recommends the “best match” candidates or identifies those with the highest potential to succeed, job seekers and even some HR professionals may feel confused, which conflicts with the TE principle. Finally, because GenAI’s race and gender biases appear to be a “global” issue, collaborative efforts across disciplines and regions are needed to address the RSPP and Acc principles. To address these concerns, Hyatt constantly monitors the potential biases that emerged from its GenAI hiring systems (Hyatt, n.d.).

Phase 3: Conduct and Rate Job Interview(s)

HR managers using GenAI to conduct and assess interviews can see improvement in efficiency and reduced scheduling pressures (Wu, 2024). Job seekers can also benefit from GenAI-powered mock interviews and feedback, which can improve their preparedness and reduce anxiety (Stone et al., 2024). The cases of Hilton and Chipotle’s Ava Cado, where GenAI assists in scheduling interviews, also provide strong evidence that GenAI tools such as HireVue and chatbots can improve efficiency and scalability in hiring (McLaren, 2018; I. Thomas, 2025). The increase in capabilities aligns well with the GW principle, offering a scalable and standardized interview process. Numerous recent studies, however, have raised critical ethical concerns about using GenAI tools to conduct or rate job interviews. Mujtaba and Mahapatra (2025) observed that GenAI-powered video interview assessments produced different personality and interview scores when a single protected characteristic, such as age, gender, or ethnicity, was altered in counterfactual candidate videos generated using generative adversarial networks. Their results showed that male, older, or African American job candidates often receive lower interview and favorable trait scores. From the job seekers’ perspective, Heo et al. (2025) found that candidates’ experiences can be shaped by gender-stereotyped voices in AI interviews, which may influence self-presentation and their interview performance. Generally, they reported that candidates had a more positive overall experience in an interview with a feminine AI voice than a masculine AI voice. Park and Jung (2025) compared U.S. and Korean job applicants’ perceptions of justice and innovativeness in GenAI-based interviews. They found that Americans perceived AI-based interviews as less fair in job relatedness, opportunity to perform, and two-way communication. Canagasuriam and Lukacik (2025) indicated that job candidates may gain an unfair advantage over others when using ChatGPT to auto-generate interview responses during asynchronous interviews. The above empirical evidence raises fairness and equity concerns for both interviewers and interviewees, potentially violating the FD and Acc principles. In addition, companies rarely disclose the algorithms used to score and rank candidates, which conflicts with the TE principle. Similar to the McDonald’s data breach case (Bradley, 2025), companies can violate the RSPP principle if they fail to safeguard job candidates’ data in this process.

Phase 4: Administer and Evaluate Assessment Tests if Applicable

GenAI-powered assessment platforms have enabled organizations like Hilton and Hyatt to automate assessment testing using gamified or interactive formats (Hyatt, n.d.; McLaren, 2018). These technological tools enable organizations to assess diverse talents with high efficiency, meeting the GW principle. Nevertheless, although self-directed interactive games and problem-solving simulations can significantly enhance efficiency, candidates unfamiliar with the new test formats may experience disadvantages, raising concerns regarding the FD principle. Additionally, companies may violate the TE principle by failing to inform candidates how they could be placed higher in the pool. Then, candidates who use GenAI to find answers for assessment tests may misrepresent their capabilities, challenging the Acc principle. Likewise, Canagasuriam and Lukacik (2025) found that higher scores in GenAI-assisted assessment may not reflect candidates’ genuine competence, challenging GenAI’s Acc principle. In another study, Phillips and Robie (2024) also found that LLMs could score higher in HR personality assessments than student participants, particularly for single-stimulus questions (i.e., agree/disagree to a single statement), as compared to phase-based forced-choice ones (i.e., choose or rank a series of statements to best describe them). Thus, organizations must make a continuous effort to ensure integrity when building or adopting GenAI assessment tools to uphold the FD, RSPP, and Acc principles.

Phase 5: Perform Background Checks

A good number of H&T companies and their hiring managers admitted that they would review the information posted on job candidates’ social media accounts before making a hiring decision (Kwok & Muñiz, 2021). GenAI can be a powerful tool in generating narrative summaries about job candidates based on their digital footprints, especially those already holding managerial or executive positions. This benefit aligns with the GW principle. However, this approach may not work fairly for all candidates, such as those applying for entry-level positions or students who lack an online presence or a digital personal brand. As reported in a relevant study by Armstrong et al. (2024), GenAI-generated content, such as resumes, will inevitably reflect racial and gender biases embedded in training data. Although generating a resume based on a name (as in the experiment by Armstrong et al.) differs from generating content about candidates based on their online footprints, GenAI may also produce biased narratives based on candidates’ names. Hence, using GenAI in background checks might not be as reliable and should be practiced with caution. Most of all, the absence of consistent online data across all candidates at various levels may result in limited or biased evaluations, raising concerns under the FD principle. Then, when companies usually do not share those GenAI-generated summaries with the candidates, they risk violating the TE principle. Other concerns regarding the RSPP and Acc principles may arise when GenAI uses outdated, incorrect, or even misleading information to generate the narratives. Another black box in hiring can also be created when HR managers fail to provide candidates with informed consent or the opportunity to articulate or clarify the information discovered in GenAI-generated narratives. If not properly handled, using GenAI for background checks might compromise data privacy and undermine organizational accountability.

Phase 6: Extend a Job Offer

GenAI tools can help both HR managers and job seekers summarize workforce and compensation trends and draft/review labor contracts, aligning with the GW principle. This is particularly relevant in markets where pay transparency laws are implemented (Baker et al., 2023). However, because LLMs heavily rely on historical salary data in training, AI-generated insights might still reflect pay disparities across gender, race, and job roles (An et al., 2024; Armstrong et al., 2024). Then, GenAI may unintentionally replicate those inequities for new hires, potentially violating the FD principle. While research has shown that companies displaying AI transparency information can increase job seekers’ attitudes toward the company, trust, and positive word-of-mouth intentions (Xiong & Kim, 2025), not every company will provide clear explanations of how compensation is determined. Job seekers may not fully understand GenAI’s role in wage determination, potentially in conflict with the TE principle. Meanwhile, privacy concerns may still arise when job seekers can use GenAI to evaluate contract terms containing sensitive personal information. Without sufficient safeguards, using GenAI in this phase still challenges the RSPP principle. Last, if there is a dispute due to misinformation, it is unclear who holds accountability, raising concerns under the Acc principle.

Overall Assessment

Generally, our critical assessment suggests that using GenAI in hiring supports the GW principle as it brings in considerable efficiency and scalability gains. Meanwhile, these gains could disproportionately benefit large H&T corporations (compared to SMEs) or job seekers with easy access to advanced GenAI tools (compared to underrepresented groups). It is worth noting that using GenAI in hiring can pose high ethical risks, with the potential to breach the FD, TE, RSPP, and Acc principles across all six hiring phases. Drawing on our analysis to answer RQ1 and RQ2, we develop the following propositions to summarize the current state of GenAI practices in hiring across the six hiring phases:

Phase 1 (P1): Companies using GenAI job application portals can significantly improve job seekers’ application experiences and the completion rate. However, improved transparency and proactive measures to mitigate biases in job postings are needed to ensure fairness among underrepresented job seekers.

Phase 2 (P2): GenAI-assisted pre-screening tools help organizations efficiently manage large applicant pools but may exacerbate disparities in hiring outcomes unless complemented with de-biasing algorithms. Underrepresented job seekers may be particularly disadvantaged if they lack access to GenAI tools or are unaware of how GenAI filters candidates.

Phase 3 (P3): GenAI-powered interviews offer scalability and convenience for organizations, yet job seekers often view them as less procedurally fair than human-led interviews. Such perception can be improved if employers clearly communicate how GenAI evaluates interviews and ensure the GenAI systems are free of biases.

Phase 4 (P4): Interactive GenAI assessments can streamline hiring but may inadvertently favor applicants familiar with GenAI tools. Without clear instructions and integrity safeguards, job seekers may game the system or misrepresent themselves, reducing its fairness and validity for both employers and job seekers.

Phase 5 (P5): GenAI can generate a narrative of a candidate, but its validity, fairness, and credibility heavily depend on the availability, accuracy, and quality of the candidate’s digital footprint. The strategic curation of a digital footprint may influence the narratives GenAI produces about a candidate and affect how the candidate is perceived in hiring processes.

P6 (Phase 6): GenAI can help employers and job seekers prepare, review, and negotiate employment contracts. However, its usefulness may be contingent upon the system’s ability to address the historical bias and provide clear explanations.

Overall, to promote trustworthy and human-centric AI, H&T organizations must not only adopt the GenAI tools in hiring responsibly, but also invest in safeguards, candidate education, and governance mechanisms aligned with the OECD AI Principles. Drawing from our critical assessment of GenAI in hiring, we provide practical and research recommendations in the next two sections to answer RQ3 and RQ4, respectively.

Practical Recommendations on How to Remain Competitive, Relevant, and Compliant With the OECD AI Principles

GenAI can help H&T organizations, from large corporations to SMEs, improve talent acquisition and hire top candidates (Garcia & Kwok, 2025). Today, leveraging technologies to manage top talent is commonly recognized as an essential competitive advantage for businesses (S. L. Thomas & Ray, 2000). Thus, H&T companies must adapt to the transformative changes brought by GenAI to remain relevant, competitive, and compliant with the OECD AI Principles. Building on our answers to RQ1 and RQ2, we advance a series of specific and actionable practical recommendations in Figure 3 to help H&T businesses and their HR managers who use GenAI in hiring, as well as job seekers who use GenAI in job applications. We expect that these actionable items will help them quickly adapt to the new AI-mediated process in each of the six hiring phases. We provide strategic recommendations as follows, rather than reiterating the items in Figure 3.

For H&T Firms and Their HR Managers

We strongly encourage both large H&T corporations and SMEs to actively integrate GenAI tools throughout their hiring process to streamline operations and improve efficiency (e.g., Budhwar et al., 2023; Garcia & Kwok, 2025; Rane, 2024; I. Thomas, 2025). GenAI can automate time-consuming, labor-intensive tasks, such as resume screening, scheduling, and real-time communication, allowing HR and hiring managers to focus on more strategic decisions. Tools like chatbots and virtual concierges in hiring can also extend an H&T company’s hospitable brand by delivering timely and personalized engagement to job seekers (Black & van Esch, 2020; Greenberg, 2025; Meister, 2018). These operational benefits should not compromise the ethical use of GenAI. Numerous H&T and HR scholars have raised valid concerns regarding transparency, biases, and accountability when companies use GenAI across multiple phases in the hiring process (e.g., Andrieux et al., 2024; Budhwar et al., 2023; Dwivedi et al., 2021, 2024; Ekuma, 2024; Kim et al., 2025). We encourage H&T companies to also use the OECD AI Principles to guide their GenAI hiring process and maintain human oversight. In this context, human oversight can be defined as crucial human involvement and judgment in GenAI applications to ensure ethical practices, mitigate biases, and make nuanced decisions (Garcia & Kwok, 2025). While H&T SMEs may face more constraints than large corporations in adopting GenAI in hiring (e.g., limited budgets for training or tech infrastructure), many may still leverage third-party platforms, such as HireVue or Pymetrics, to implement GenAI affordably. Regardless of size, all H&T organizations must remain vigilant in reviewing and updating their GenAI practices, such as data privacy policies, AI usage guidelines, and staff training programs, to reflect their organizational values and ethical commitments.

For Job Candidates, Including College Students

Job seekers must also adopt GenAI as a valuable and supporting source throughout the job search journey, from identifying best-fit opportunities to offer negotiations. GenAI can help candidates tailor their resumes and cover letters, identify skill gaps, and generate personalized interview practices. Meanwhile, candidates must uphold integrity while using GenAI in job searches. Submitting original materials, safeguarding personal data, and avoiding overreliance on AI-generated content are essential in building trust with employers. Understanding how GenAI tools may shape a job candidate’s personal brand is a good starting point for establishing an authentic professional image in the digital world. In doing so, job seekers contribute to the OECD’s vision of having a more trustworthy and human-centric AI (hiring) ecosystem. For early-career candidates and students, GenAI can be valuable for long-term career planning. Beyond its assistance in employment, GenAI can support H&T students’ graduate school applications, such as refining personal statements, preparing for entrance or professional exams (e.g., Graduate Record Examination [GRE], General Management Admission Test [GMAT], and Certified Public Accountant [CPA]), and practicing for admission interviews.

Besides the H&T Firms, Their HR Managers, and Job Seekers

When job seekers are also students, H&T professors and career coaches are uniquely positioned to help them navigate the evolving GenAI hiring process with personalized advice and industry-specific insights. With a mission to promote student success, they play a critical supporting and mediating role between students and H&T companies in the GenAI hiring ecosystem. We recognize their valuable contributions and emphasize the importance of stronger academic-industry collaborations at this critical point. Particularly, we encourage them to actively engage with their industry partners to co-develop updated career management workshops that address GenAI's role throughout the six phases of the hiring process. These collaborative efforts can align students’ career readiness with real-world hiring practices while upholding high ethical standards, ultimately addressing the industry's labor shortage challenge (Kwok, 2022). In addition, H&T instructors can gain additional GenAI insights about labor market trends. H&T programs can leverage GenAI to analyze their students’ skill gaps. Then, they can redesign a curriculum that bridges students' skill gaps with the updated industry expectations. When students are empowered with the skills desired by H&T employers, they are better prepared for a successful career. Additionally, students must understand the essential tactics to build an authentic digital brand as well as the security measures to protect their private information in the cyber marketplace. Teaching students how to use GenAI tools strategically and responsibly is an excellent way to support the OECD vision of promoting trustworthy and human-centric AI usage.

Proposed Research Ideas to Respond to the Updated GenAI-Assisted Hiring Process

According to a systematic review of AI research published between 1991 and 2021 by Kong et al. (2023), it was not until 2019 that AI studies in the H&T field showed exponential growth in numbers. Nevertheless, much of the research merely focused on how consumers accepted or evaluated AI technology (Kong et al., 2023). Law et al. (2024) conducted another systematic review of 47 AI studies published between 2021 and April 2023 and reached a similar conclusion: consumers’ service encounters with AI remained a dominant focus of relevant literature. Research about consumers’ or employees’ perceptions of AI implications is much needed and can bring valuable insights to H&T operations. Meanwhile, leaders and managers in the corporate world have shifted their priorities to embedding AI into business functions and using AI to hire the best talent (Rowan et al., 2024). In particular, HR has become the fastest-growing area for corporate AI spending (Yam & Skorburg, 2021), and it is the area where businesses can quickly see meaningful cost reductions due to AI integration (Singla et al., 2024). Since the global spending on AI solutions is projected to grow from $40.5 billion in 2024 to $202.2 billion in 2028, or fivefold in 5 years (Lin, 2025), we expect that more H&T firms will increase their AI investment in various HR functions, including using AI to streamline the hiring process.

Generally, there is a consensus among academic scholars and industry professionals about GenAI’s powerful ability to streamline many aspects of hiring (e.g., Budhwar et al., 2023; Garcia & Kwok, 2025; Rane, 2024; Wu, 2024). Nevertheless, in light of the OECD’s vision of building trustworthy and human-centric AI, several important questions, particularly about fairness, transparency, accountability, and data protection, seemingly remain underexplored and need immediate research attention. Unlike conventional systematic or narrative review articles, we adopt a reflective approach in this work to raise a series of research questions (see Table 1), drawing on our field experience, domain knowledge about recruitment and selection, and, more importantly, our answers to RQ1 and RQ2 in the earlier sections. Carrying a similar objective as other critical reflection or foresight articles (e.g., Andrieux et al., 2024; Cha et al., 2025; Kwok, 2022; Yang & Wang, 2025), we aim to initiate a dialogue with the questions in Table 1 among H&T, OB, and HR scholars about a contemporary issue: GenAI in hiring.

Table 1

Specific Research Questions Regarding the AI-Assisted Hiring Process.

Steps	H&T Companies and HR Managers	Job Seekers, Including College Students
1. Accept Job Applications and Resumes	• (GW) What standardized measures can be used to monitor and validate AI-assisted hiring tools? • (GW) What obstacles must H&T organizations of different sizes (e.g., SMEs vs. big corporations) overcome to implement GenAI in the hiring process? • (GW) To ensure equitable access to job application portals, how can companies address the barriers faced by applicants with varying levels of digital literacy about GenAI? • (FD) What measures must be taken to ensure fairness among job applicants with diverse backgrounds? • (FD) How effective are GenAI tools in recruiting underrepresented groups in the H&T industry? • (TE) What is the most effective communication method (e.g., visual aids, plain language) to help job applicants understand GenAI-assisted web portals? • (RSPP) What is the ultimate proficiency level (effectiveness vs. efficiency) to ensure cybersecurity and privacy protections for job candidates’ personal data? • (Acc) What appeal systems or support resources are necessary for job seekers if their application is mishandled?	• (GW) How does the AI-assisted hiring process affect job seekers’ perceptions of a company’s employment brand through their job search experiences? • (GW/FD) What factors affect job seekers’ confidence, anxiety, stress, well-being, and perceived fairness about the GenAI-assisted hiring process? • (TE) What preferred communication channels and methods do job seekers want? • (RSPP) What cybersecurity and privacy concerns and barriers do job seekers have when engaging in the GenAI-hiring process? • (Acc) How do job seekers attribute responsibility for errors, fairness, and misinformation among organizations, the technological agencies that manage web portals and data, and themselves?
2. Screen Applications and Resumes	• (GW) What are the best practices for integrating AI-driven and human-centric resume screening procedures? • (GW) How valid are AI resume screening results in predicting a candidate's short-term vs. long-term success, especially in the H&T industry with a high turnover rate? • (FD) What measures must be taken to mitigate the potential biases in resume screening? • (TE) To what extent do HR managers understand and trust the logic behind GenAI recommendations about the candidates? • (TE) What kind of GenAI outputs (e.g., numerical scores or descriptive rationales) are most helpful for HR managers to justify their hiring decisions? • (RSPP) What are the similarities and differences of GenAI secure measures and compliance across various regions of H&T operations? • (Acc) What appeal procedures or override mechanisms should be in place when a qualified applicant is mistakenly excluded by a GenAI tool?	• (GW) What affects job candidates’ perceived fairness and trust in GenAI resume screening tools? • (GW/FD) What support can be helpful for job seekers from underrepresented or marginalized communities? • (TE) What is an effective way to communicate with job seekers regarding how GenAI screening tools work in evaluating resumes? • (RSPP) What security measures can enhance job seekers’ level of confidence in uploading their resumes, along with other personal data? • (Acc) To what extent should job candidates polish their application letters and resumes to remain authentic personal brands in their job searches?
3. Conduct and Rate Job Interview(s)	• (GW/Acc) What is the ultimate balance between using AI and HR managers in job interviews? • (GW) How effective is AI in assessing soft skills, emotional intelligence, team fit, and behavioral traits compared to traditional interview methods? • (GW) How valid can AI interview evaluations predict a candidate's short-term vs. long-term success, especially when the H&T industry has a high turnover rate? • (FD) How fairly do GenAI tools evaluate candidates’ interview performance, regardless of their cultural and demographic backgrounds? • (TE) What level of explanation do HR managers need to confidently accept GenAI evaluations of candidates? • (RSPP) What procedures are in place to securely record, store, and manage candidates’ interview recordings? • (Acc) What procedures are currently in place, or should be developed, to override GenAI recommendations when qualified candidates are wrongfully rejected?	• (GW) What affects job candidates’ AI interview experiences? • (GW) What can be done to improve their AI scheduling and interview experiences? • (GW/FD) How do GenAI tools at this stage affect job seekers’ well-being, anxiety, stress, trust, and perceived fairness of the ranking system? • (FD) What training or support can H&T companies provide to less tech-savvy groups or otherwise disadvantaged groups to help them reduce their anxiety when participating in GenAI interviews? • (TE) What types of feedback and explanations should be provided to candidates, particularly those that help build trust in the employment brand? • (RSPP) What are candidates’ comfort levels regarding how their recorded interviews are stored (e.g., where and for how long), accessed (e.g., by whom), and managed? • (Acc) What types of accountability statements can reduce candidates’ intention to appeal or disagree with the GenAI recommendation?
4. Administer and Evaluate Assessments	• (GW) How much improvement can be achieved if using AI-assisted vs. regular computer-based assessment tools? • (GW) How can adaptive testing powered by GenAI reduce hiring bottlenecks and improve placement outcomes in large-scale seasonal hiring in the H&T industry? • (FD) What measures should be taken to ensure the fairness of AI assessments among job candidates with diverse backgrounds and proficiencies in AI? • (TE) What types of explanations or scoring methods would increase hiring managers’ confidence in GenAI assessment tools? • (RSPP) What data governance frameworks are (or should be) in place for storing and reusing assessment performance data, especially those about job candidates’ personalities? • (Acc) What auditing and human-review processes are (or should be) in place to ensure accountability in final hiring decisions based on GenAI assessments?	• (GW) How do GenAI assessments affect candidates’ perception of the H&T company’s employment brand, especially when they are rejected? • (FD/Acc) What are the ethical considerations of using GenAI in assessments (for both employers and job candidates)? • (FD/TE) What types of explanations or scoring methods would increase job seekers’ confidence, perceived validity, and perceived fairness of the GenAI assessment tools? • (TE) What kind of post-assessment feedback (if any) will increase job seekers’ trust in GenAI assessment and the H&T company’s employment brand? • (RSPP) How do candidates react to various data security/management procedures for their assessment results? • (Acc) What dispute or human review options do job candidates expect at this stage?
5. Perform Background Checks	• (GW) How effective are GenAI background checks compared to traditional background checks (e.g., leadership potential, soft skills, emotional intelligence, and team fit)? • (GW) How do GenAI background checks lead to more timely and holistic hiring decisions? • (GW) What are the correlations between GenAI background check results and the candidates’ long-term commitment to the employer (high turnover in H&T)? • (FD) What actions can be taken to minimize GenAI bias in public web content (e.g., mainstream media outlets vs. personal blogs) that may unfairly shape candidates’ narratives? • (TE) What level of explanation is necessary for HR managers to accurately interpret AI-generated narratives? • (RSPP) What safeguards exist to prevent incorrect data or identity confusion when using GenAI background checks? • (Acc) To what extent should companies allow job candidates to verify (and clarify if needed) the AI-generated narrative without sacrificing efficiency?	• (GW) What are the effective ways to establish an ideal personal brand tailored to GenAI search results? • (GW) What type of digital footprint (e.g., videos vs. text, self-generated content vs. press-release articles) will be more effective in shaping a job candidate’s GenAI narrative? • (FD) To what extent (if any) would job candidates without curated online identities face systemic disadvantages in GenAI-assisted background checks? • (FD/TE) What types of disclosure would increase candidates’ confidence, perceived validity, and perceived fairness of the AI-generated narrative? • (RSPP) How secure and ethical is the storage or reuse of their digital background data from the AI-generated narrative? • (Acc) What expectations do candidates have for human intervention and interpretation of GenAI background checks, especially if a dispute arises?
6. Extend a Job Offer	• (GW) How reliable are AI-generated reports on competitive wages, labor conditions, etc.? • (FD) How much do the AI-generated results about wages and compensation reflect and perpetuate historical pay inequities (e.g., gender gap)? • (FD/RSPP) What measures must be taken to strike a balance between wage transparency and data privacy? • (TE) How would transparency at this stage affect candidates’ trust in GenAI results and their intentions to accept the job offers? • (Acc) To what extent should HR managers be empowered and trained to critically review GenAI suggestions before finalizing offers?	• (GW) How can job seekers leverage AI insights when negotiating for the best and fair labor contract? • (FD) How will GenAI encourage certain groups (e.g., women, first-time job seekers, non-native speakers) to be more engaged in the negotiation of the final offer? • (TE) What type of GenAI outputs (e.g., charts vs. percentile rankings) are most helpful for candidates to negotiate better working conditions? • (RSPP) After candidates successfully negotiate a compensation package, how sensitive do they become when they are asked to share sensitive compensation and employment data with GenAI tools? • (Acc) Who is responsible for the consequences if a candidate makes a decision based on faulty or misleading GenAI advice?

Note. GW = growth & well-being, FD = fairness & diversity, TE = transparency & explainability, RSPP = robustness, security, & privacy protections, and Acc = accountability.

In terms of research ideas with direct implications for H&T companies and HR managers, we advocate more efforts to address the equity, accessibility, and fairness concerns, a direction aligning OECD’s GW and FD principles and being supported by other scholars with similar interests (e.g., Andrieux et al., 2024; Wong et al., 2025). Particularly, we encourage future research to further validate GenAI’s ability to assess candidates’ soft skills, emotional intelligence, and team fit, the essential skills among H&T employees (Kwok & Muñiz, 2021; Liu-Lastres et al., 2024; Wong et al., 2025). Then, to address the “black box” nature of GenAI in hiring and the TE principle, we raise a series of questions regarding business communications and explainable AI outputs. Next, we also propose numerous urgent questions to address concerns regarding privacy, consent, data security, and the validity of GenAI analytic results, as we understand that GenAI models heavily rely on large historical datasets, addressing the RSPP and Acc principles. Finally, because the H&T industry is traditionally known for its challenging labor issues (see Hamouche et al., 2023; Kwok, 2022; Liu-Lastres et al., 2023, 2024), we are interested in GenAI’s long-term effects on the workforce, such as turnover and trust in the employment brand.

Regarding research ideas that are highly relevant to job seekers in the H&T industry, we focus on empowering candidates, allowing them to seize an opportunity in a fair, respectful, trustworthy, and supportive job market. Besides questions addressing the GW and FD principles, we advocate for research efforts under the TE principle to address the “black box” concern in GenAI hiring, such as testing how different types of explanations, feedback, and communication channels/methods may support job candidates in the process. Additionally, we encourage future research to investigate candidates’ expectations of perceived risks, human intervention, responsibility attribution, and ethical boundaries of GenAI tools in hiring.

Regarding the appropriate methodologies to address the research questions proposed in Table 1, we recommend that empirical investigations adopt a diverse and rigorous set of methodological approaches to study the still-evolving GenAI-assisted hiring process. Experimental designs, such as randomized field experiments and quasi-experiments, can be meaningful for evaluating how the use of GenAI in hiring affects fairness perceptions, trust, candidate performance, and decision outcomes across various hiring phases. For instance, a field experiment could compare applicant reactions to AI-generated versus human-generated feedback on asynchronous interviews, capturing effects on transparency perceptions, procedural fairness, and an H&T organization’s brand image, particularly within SMEs and among underrepresented groups. Longitudinal designs, such as panel or cohort studies, offer a way to unfold GenAI’s long-term impacts on organizations and the workforce. For example, a panel study that tracks GenAI hiring outcomes, such as retention, job satisfaction, performance, career progression, and trust in the organization, can help assess the predictive validity and ethical sustainability of using GenAI in hiring over time.

Additionally, qualitative methods, such as semi-structured interviews and the critical incident technique, are well-suited to exploring HR managers’ and job seekers’ experiences with GenAI tools used in hiring. These approaches can reveal nuanced perceptions of fairness, data privacy concerns, and psychological responses to AI interventions that quantitative surveys may not easily capture. Computational techniques, such as NLP, can complement qualitative designs by analyzing large volumes of texts generated by AI systems or job candidates. Exploratory designs in mixed-methods research can be valuable for scale development or when existing measurements of constructs (e.g., perceived fairness or explainability of GenAI) are underdeveloped (Kwok, 2012). Ultimately, we encourage researchers to adopt a creative and methodologically pluralistic mindset as they investigate this complex, dynamic phenomenon. Because GenAI in hiring spans technical, ethical, and behavioral domains, we particularly welcome interdisciplinary studies that integrate diverse theories and methodologies to advance deeper, more holistic insights.

Conclusion

We recognize that H&T businesses need to revolutionize their hiring process to remain competitive in the marketplace. We first reviewed GenAI’s transformational changes to the hiring process that affect the H&T industry’s two primary stakeholders: organizations and their HR managers who perform the hiring, and job seekers who go through the process. We summarized our narrative analysis in Figure 2. Then, we used OECD’s five core AI Principles to critically assess GenAI’s impacts on hiring, which informed us to advance six propositions to summarize the current state of GenAI practices. Also referring to the OECD’s vision and our assessment of GenAI in hiring, we advanced a series of practical recommendations in Figure 3 to help H&T companies, their HR managers, and job seekers remain competitive, relevant, and compliant with the OECD’s AI Principles. We concluded our discussion by proposing new ideas with specific research questions in Table 1, along with the appropriate methodologies that can be used for empirical investigations. Our work has valuable practical and research implications to inspire relevant research that directly benefits the primary H&T stakeholders we serve. Beyond H&T, this reflection also carries broader societal implications. As GenAI becomes more embedded in workforce development pipelines, biases, lack of transparency, and breaches of data ethics in GenAI-assisted hiring can lead to adverse system-wide consequences, affecting opportunity access, labor equity, and algorithmic accountability on a broader scale. Furthermore, by applying the OECD AI Principles to critically assess GenAI in hiring, this study draws inspiration from H&T, information technology, information systems, organizational behavior, human resource management, policymaking, and business ethics literature. Its interdisciplinary orientation reflects the complex, cross-sector nature of AI adoption, highlighting the need for collaborative efforts among researchers, policymakers, and educators to shape more inclusive, ethical, and future-ready employment ecosystems.

We also acknowledge the limitations of this reflection. First, adopting a reflective approach, we discussed four research questions related to a contemporary issue through our interpretation of limited sources of literature and industry reports. We encourage readers to use careful judgment on how our conclusions and their implementation apply to their work. Second, due to our reflective approach and the ongoing evolution of GenAI, our discussion may have overlooked some critical examples and cases of how H&T organizations, particularly SMEs, utilize GenAI in hiring. Third, this reflection has a narrow focus on the two primary stakeholders we serve, but AI has affected all stakeholders involved in the hiring process. Finally, like GenAI’s debut in 2022, the next and even newer generation of AI applications (e.g., reasoning and agentic AI) could arrive soon, possibly causing another disruption to the existing hiring process. We hope the proactive approaches discussed in this paper can better prepare us for the next generation of AI and a new wave of technological revolution in the global economy.

Footnotes

Acknowledgements

The authors thank R. L. Fernando Garcia, DBA, for creating the artwork of all figures in this paper.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Linchi Kwok

Ning-Kuang Chuang

Yung-Kuei Huang

Author Biographies

Linchi Kwok, Ph.D., is a professor in the Collins College of Hospitality Management at California State Polytechnic University Pomona (Cal Poly Pomona). He received an M.S. in Restaurant, Hotel, and Institutional Management and a Ph.D. in Hospitality Administration from Texas Tech University, as well as an MBA from Syracuse University. His research interests include hospitality management, information technology, and service marketing. https://orcid.org/0000-0001-8838-2485

Ning-Kuang Chuang, Ph.D., is an associate professor in the School of Foundations, Leadership and Administration at Kent State University. She received a B.S. degree in Hospitality and Tourism Management from Purdue University, a master’s degree in Foodservice and Food Studies from New York University, and a Ph.D. degree in Hospitality Administration from Texas Tech University. Her research interests include career development and organizational behavior. https://orcid.org/0000-0002-7785-5002.

Yung-Kuei Huang, Ph.D., is an associate professor in the Department of Leisure Industry and Health Promotion at National Ilan University, Taiwan. He received an M.S. degree and a Ph.D. degree in Recreation, Sport, and Tourism from the University of Illinois at Urbana-Champaign. He holds a bachelor’s degree in Finance from National Chengchi University, Taiwan. His research interests include human resources, organizational behavior, and sustainability in tourism and hospitality. https://orcid.org/0000-0001-9203-9671.

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