Please use this identifier to cite or link to this item:
Bias Mitigation for Machine Learning Fairness - Job Recruiting Selection as an Example
|Issue Date:||2021-03-02 14:56:20 (UTC+8)|
In the past, we intuitively believed that machine learning should be fair and neutral, because it comes from mathematical calculations and statistics. But this is not the case. Machine learning learns through training data, so it is inevitable that it will also learn human discrimination and prejudice. Bias is necessary in machine learning. It can also be said that a model trained on an unbiased data set has not learned any knowledge, and its classification results have no reference value. But sometimes the bias comes from sensitive or protected attributes, which can cause unfairness and illegality.
The purpose of this paper is to use recruitment as the theme to discuss the pre-processing algorithm to achieve the goal of reducing machine learning discrimination and prejudice, and to use Scikit-learn and IBM AIF360 library to construct a standardized deflection reducing machine learning process. It has been experimentally verified that the pre-processing algorithm reduces the marital bias of the data set, which can make the model more fair, make the classification results of the married and unmarried ethnic groups more consistent, and improve the overall accuracy and classification of the classifier model quality.
|Reference:|| Acharyya, Rupam, et al. "Detection and Mitigation of Bias in Ted Talk Ratings." arXiv preprint arXiv:2003.00683 (2020).|
 Angwin, Julia, et al. (2016). “Machine bias. ProPublica.”, https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing, accessed: 2020-03-13
 Bellamy, Rachel KE, et al. "AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias." arXiv preprint arXiv:1810.01943 (2018).
 Calders, Toon, Faisal Kamiran, and Mykola Pechenizkiy. "Building classifiers with independency constraints." 2009 IEEE International Conference on Data Mining Workshops. IEEE, 2009.
 Chouldechova, Alexandra, and Aaron Roth. "The frontiers of fairness in machine learning." arXiv preprint arXiv:1810.08810 (2018).
 d'Alessandro, Brian, Cathy O'Neil, and Tom LaGatta. "Conscientious classification: A data scientist's guide to discrimination-aware classification." Big data 5.2 (2017): 120-134.
 Dwork, Cynthia, et al. "Fairness through awareness." Proceedings of the 3rd innovations in theoretical computer science conference. 2012.
 Frida Polli ,“Using AI to Eliminate Bias from Hiring” https://hbr.org/2019/10/using-ai-to-eliminate-bias-from-hiring, accessed:2020-03-18
 Kamishima, Toshihiro, et al. "Fairness-aware classifier with prejudice remover regularizer." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2012.
 Lohia, Pranay K., et al. "Bias mitigation post-processing for individual and group fairness." Icassp 2019-2019 ieee international conference on acoustics, speech and signal processing (icassp). IEEE, 2019.
 Manish Raghavan and Solon Barocas ,“Challenges for mitigating bias in algorithmic hiring”, https://www.brookings.edu/research/challenges-for-mitigating-bias-in-algorithmic-hiring/, accessed: 2020-04-30
 Mehrabi, Ninareh, et al. "A survey on bias and fairness in machine learning." arXiv preprint arXiv:1908.09635 (2019).
 Peña, Alejandro, et al. "Bias in Multimodal AI: Testbed for Fair Automatic Recruitment." arXiv preprint arXiv:2004.07173 (2020).
 Peng, Andi, et al. "What you see is what you get? The impact of representation criteria on human bias in hiring." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. Vol. 7. No. 1. 2019.
 Qin, Chuan, et al. "Enhancing person-job fit for talent recruitment: An ability-aware neural network approach." The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018.
 Raghavan, Manish, et al. "Mitigating bias in algorithmic hiring: Evaluating claims and practices." Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020.
 Silberg, Jake, and James Manyika. "Notes from the AI frontier: Tackling bias in AI (and in humans)." McKinsey Global Institute (2019): 4-5.
 Society For Human Resource Management. 2016. 2016 Human Capital Benchmarking Report. https://www.shrm.org/hr-today/trends-and-forecasting/ research-and-surveys/Documents/2016-Human-Capital-Report.pdf. (2016).
 Trisha Mahoney, Kush R. Varshney & Michael Hind. (2020). “AI Fairness - How to Measure and Reduce Unwanted Bias in Machine Learning”, https://krvarshney.github.io/pubs/MahoneyVH2020.pdf, accessed: 2020-04-30
 Xue, Songkai, Mikhail Yurochkin, and Yuekai Sun. "Auditing ML Models for Individual Bias and Unfairness." arXiv preprint arXiv:2003.05048 (2020).
 Zhang, Brian Hu, Blake Lemoine, and Margaret Mitchell. "Mitigating unwanted biases with adversarial learning." Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. 2018.
 Zhang, Yukun, and Longsheng Zhou. "Fairness Assessment for Artificial Intelligence in Financial Industry." arXiv preprint arXiv:1912.07211 (2019).
 Ziyuan Zhong ,“A Tutorial on Fairness in Machine Learning”, https://towardsdatascience.com/a-tutorial-on-fairness-in-machine-learning-3ff8ba1040cb, accessed:2020-03-28.
|Appears in Collections:||[資訊科學系碩士在職專班] 學位論文|
Files in This Item:
All items in 學術集成 are protected by copyright, with all rights reserved.