Fair and Ethical Resume Screening: Enhancing ATS with JustScreen the ResumeScreeningApp
DOI:
https://doi.org/10.70715/jitcai.2024.v2.i1.001Abstract
Abstract— In today's fast-paced job market, the efficiency and
fairness of the resume screening process are paramount.
"JustScreen" emerges as a cutting-edge solution leveraging
advanced Natural Language Processing (NLP) to automate
resume evaluation, thus eliminating biases and promoting merit-
based candidate selection. This thesis explores JustScreen's
innovative approach to integrating NLP and machine learning
algorithms to enhance the recruitment workflow, ensuring a more
streamlined, unbiased, and efficient candidate assessment process.
The methodology involves several key components: data
preprocessing, NLP information extraction, fairness metrics
calculation, bias mitigation, and interpretability techniques. By
utilizing frameworks such as spaCy for NLP tasks, JustScreen
aims to overcome the challenges of traditional manual screening
processes, improving both accuracy and fairness. This thesis
explores the transition from developing a full Application
Tracking System (ATS) to creating a powerful enhancement for
existing ATS systems. The ResumeScreeningApp/ JustScreen
integrates generative AI to provide comprehensive resume
analysis, adding significant value to traditional ATS
functionalities. Initial evaluations indicate a significant
advancement in talent acquisition practices, promoting equal
opportunities and reducing the impact of potentially
discriminatory factors. This research signifies a transformative
shift in recruitment, setting new standards for ethical and efficient
hiring practices using Generative AI.
Downloads
References
[1] Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2019). Mitigating bias in algorithmic hiring: evaluating claims and practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3351095.3372828 DOI: https://doi.org/10.1145/3351095.3372828
[2] Binns, R. (2017). Fairness in Machine Learning: Lessons from Political Philosophy. Decision-Making in Computational Design & Technology eJournal. https://doi.org/10.48550/arXiv.1712.03586
[3] Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017). Algorithmic Decision Making and the Cost of Fairness. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17). Association for Computing Machinery, New York, NY, USA, 797–806. https://doi.org/10.1145/3097983.3098095 DOI: https://doi.org/10.1145/3097983.3098095
[4] Holstein, K., Vaughan, J. W., Daumé, H., Dudik, M., & Wallach, H. (2019). Improving Fairness in Machine Learning Systems: What Do
Industry Practitioners Need? In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). Association for Computing Machinery, New York, NY, USA, Paper 600, 1–16. https://doi.org/10.1145/3290605.3300830 DOI: https://doi.org/10.1145/3290605.3300830
[5] Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015). Certifying and Removing Disparate Impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '15). Association for Computing Machinery, New York, NY, USA, 259–268. https://doi.org/10.1145/2783258.2783311 DOI: https://doi.org/10.1145/2783258.2783311
[6] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019). A Survey on Bias and Fairness in Machine Learning. ACM
Computing Surveys (CSUR), 54, 1 - 35. https://doi.org/10.1145/3457607 DOI: https://doi.org/10.1145/3457607
[7] Corbett-Davies, S., & Goel, S. (2018). The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning.
https://doi.org/10.48550/arXiv.1808.00023
[8] Kearns, M., Neel, S., Roth, A., & Wu, Z. (2017). Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness.
https://doi.org/10.48550/arXiv.1711.05144
[9] SpaCy. (n.d.). Industrial-Strength Natural Language Processing in Python. Retrieved from https://spacy.io
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Gloribeth Navarro (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
