What is the evidence on AI use in job applications and hiring?

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1. 1

   Mapping the use of large language models in hiring decisions: a scoping review.

   Frontiers in artificial intelligence (Tripathi et al.)Published Mar 13, 2026Checked Oct 14, 2026

   “Unlike earlier algorithmic hiring tools, LLMs operate through language-mediated interaction, influencing interpretive and evaluative layers of decision-making. This scoping review maps the academic literature on LLMs in hiring to examine (i) where and how these systems are applied across the hiring pipeline, (ii) what forms of evidence and outcomes are assessed, (iii) which risks and mitigation strategies are documented, and (iv) how disciplinary structures shape research focus. Following PRISMA-ScR guidelines, we synthesize research published between 2018 and 2026 across multiple disciplines using a transparent, lexicon-based coding approach. The results reveal a rapidly expanding but uneven literature, characterized by concentration in early hiring stages, selective outcome measurement favoring efficiency and performance, high awareness of ethical risks with limited empirical validation of controls, and structurally constrained interdisciplinarity. The review highlights key gaps and provides a foundation for future interdisciplinary and field-based research on responsible LLM use in hiring.”

2. 2

   Explore barriers and risks that hinder AI integration into global talent management: A Systematic Review.

   F1000Research (Maake et al.)Published May 25, 2026Checked Oct 14, 2026

   “A systematic literature review was undertaken in accordance with PRISMA-ScR standards. The review searched eight databases (Google Scholar, Scopus, Web of Science, ScienceDirect, ProQuest, Emerald Insight, Sabinet, and Elsevier) using a comprehensive search approach. Articles published between 2020 and 2025 in peer-reviewed journals were considered. The entire texts of selected studies were assessed against the inclusion criteria, and 23 peer-reviewed empirical studies were included in the full review. An analysis of 23 scholarly articles identified three key barriers and risks to implementing AI within GTM, including governance and accountability, implementation hurdles, and ethical and technical concerns. The successful integration of AI into GTM depends on more than just technology. It requires a confluence of strategic vision, enabling infrastructure, cultural readiness, and ethical oversight. The risks associated with AI reflect a failure to align innovation with governance, human-centred design, and long-term trust-building.”

3. 3

   AI-based interviews and applicant perceptions: insights from China and Pakistan.

   Frontiers in psychology (Zhao et al.)Published Aug 25, 2026Checked Oct 14, 2026

   “Partial Least Squares Structural Equation Modeling was used to test the proposed relationships, followed by measurement invariance assessment and multi-group analysis.ResultsThe results show that AI-based interviews were negatively associated with perceived transparency, interactive communication, and job pursuit intention, with stronger negative associations observed among respondents in the Pakistani sample than in the Chinese sample. Moreover, trust in AI systems emerged as a significant positive moderator in both cultural contexts, suggesting that higher trust in AI weakened negative applicant responses to AI-based interviews.DiscussionOverall, the findings enhance understanding of how interview type and related perceptions shape applicant attitudes in cross-cultural recruitment settings. The study provides practical implications for HR managers and organizations, and discusses key findings, managerial recommendations, study limitations, and directions for future research.”

4. 4

   A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring

   AI (Albaroudi et al.)Published Feb 7, 2024Checked Oct 14, 2026

   “It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.”

5. 5

   Explainable AI in employment decision-making: A systematic review of transparency methods in hiring algorithms

   Issues in Information SystemsPublished Jan 1, 2025Checked Oct 14, 2026

   “ittle insight into how or why hiring decisions occur.This lack of transparency undermines fairness, accountability, and regulatory compliance, especially in contexts where bias may persist or worsen.Explainable AI (XAI) is emerging as a critical strategy to address these concerns by providing interpretable outputs that reveal the model's logic and decision-making factors.This study presents a systematic review of explainable artificial intelligence (XAI) techniques utilized in hiring and employment-related AI systems.It identifies prominent methods, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), decision trees, and counterfactuals, and evaluates their performance in terms of interpretability, fidelity, and fairness.Additionally, it examines their impact on candidate trust and organizational transparency.The findings suggest while XAI can improve understanding and reduce biases, technical complexity and human interpretability issues often limit its effectiveness.The paper concludes by recommending a human-centred approach to XAI design and the development of stronger regulatory frameworks for fair and equitable hiring practices.”

6. 6

   Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey

   ACM Transactions on Intelligent Systems and Technology (Fabris et al.)Published Sep 23, 2024Checked Oct 14, 2026

   “Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of , algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.”

7. 7

   Problematizing the role of artificial intelligence in hiring and organizational inequalities: A multidisciplinary review.

   Human relations; studies towards the integration of the social sciences (Hughes et al.)Published Dec 30, 2025Checked Oct 14, 2026

   “This review article critically advances this debate by drawing on diverse scholarship across computing and data sciences; human resource, management, and organization studies; social sciences; and law. Using a hybrid review approach that combines scoping and problematizing review methods, we examine the implications of algorithmic hiring for organizational inequalities. Our review identifies a multidisciplinary discussion marked by asymmetries in how key concerns are conceptualized; a clear and heightened potential for AI to conceal inequalities in hiring processes; and contestation over the regulation of algorithmic hiring. Building on Acker's (2006) framework of 'inequality regimes', we propose the concept of algorithmically-mediated inequality regimes to highlight AI's capacity for concealing and reproducing inequalities in hiring through enhanced algorithmic invisibility and the growing legitimacy of AI solutions. We propose an agenda for future research, policy, and practice, emphasizing the need for an interdisciplinary 'chain of knowledge' and a multi-stakeholder 'chain of responsibility' in AI application and regulation.”

8. 8

   'I applied for 200 jobs' - How 'spray and pray' AI job applications can backfire

   BBC NewsPublished Oct 14, 2026Checked Oct 14, 2026

   “Graduates are increasingly using AI to misrepresent themselves in job applications, research suggests. But some say their peer group has no choice.”