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A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring
Citation. Albaroudi, E., Mansouri, T., & Alameer, A. (2024). A comprehensive review of AI techniques for addressing algorithmic bias in job hiring. AI, 5(1), 383–404. https://doi.org/10.3390/ai5010019
Summary. This peer-reviewed literature review examines algorithmic bias in AI-supported job hiring and methods proposed to mitigate it. It identifies vector-space correction and data augmentation as the main NLP and deep-learning techniques discussed in the reviewed literature. The authors argue that technical mitigation needs human involvement in building and testing hiring applications, while noting that research on hiring algorithms remains formative.
Key claims
- AI-supported CV screening can reproduce or amplify discrimination in hiring when models inherit biased data, language, or decision patterns.
- The review identifies vector-space correction and data augmentation as techniques used to mitigate algorithmic bias in hiring-related NLP and deep-learning systems.
- The authors recommend human–machine collaboration and active recruiter testing of hiring applications rather than treating technical mitigation as sufficient on its own.
Evidence & limitations
- The paper reports a systematic literature review using searches across Google Scholar, ScienceDirect, SpringerLink, IEEE Xplore, and Wiley Online Library, with English-language inclusion criteria and title/abstract screening.
- It is a review rather than a new empirical evaluation; its conclusions about mitigation effectiveness depend on the underlying studies and do not establish a single causal estimate for hiring outcomes.