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@article{201438,
        author = {DISHA BARAPATRE and Anushka Adak and Aarya Nalawade and Gargi Pawar and Sayali Joshi},
        title = {Resume Bias Detection and Mitigation using Artificial Intelligence and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3629-3634},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201438},
        abstract = {Automated resume screening software, although highly efficient, often perpetuates historical biases associated with gender, geography, and educational backgrounds. The current work develops a stage fairness pipeline framework involving detection, mitigation through several techniques, and audit based interpretability. Using four empirical studies' results, this paper shows that adversarial debiasing outperforms any other form of debiased pre-processing as it yields DPD/EOD equal to 0 without any loss of predictive accuracy. We introduce two new concepts Checkpoint and Automated Bias Audit Report as part of the paper. Intersectional bias is recognized as a major overlooked issue in algorithmic discrimination in recruitment practices.},
        keywords = {AI Bias, Recruitment Algorithms, Disparate Impact, Algorithmic Fairness, Machine Learning Ethics, HR Technology},
        month = {May},
        }