Does Data-Driven Recruitment Lead to Less Discrimination? – A Technical Perspective

Bibtex

Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "Does Data-Driven Recruitment Lead to Less Discrimination? – A Technical Perspective", 
							Author= "Kian Schmalenbach, Sven Laumer", 
							Doi= "https://doi.org/10.30844/wi_2020_q2-schmalenbach", 
							 Abstract= "Due to its large cost-saving potential, data-driven recruitment is becoming increasingly popular across various industries. However, several cases were reported where the use of corresponding technologies had caused systematic discrimination against certain candidate groups. While existing approaches to discover and prevent discrimination in data classification mostly perform well within a specific context, it remains unclear to what extent datadriven recruitment can be conducted discrimination-free in real-world business applications, where the respective context-specific assumptions do not necessarily hold. Hence, we first define a generic discrimination model that allows for arbitrary descriptions of job candidate characteristics, before applying two sophisticated discrimination-prevention algorithms on a sample data set generated from our model to evaluate the algorithms’ performance. Our analysis shows that the amount of removed discrimination highly depends on the application context and its underlying definitions and assumptions, making it hard to provide a holistic answer to our research question.

", 
							 Keywords= "data-driven recruitment, discrimination-aware data mining, discrimination discovery, discrimination prevention", 
							}
					
Kian Schmalenbach, Sven Laumer: Does Data-Driven Recruitment Lead to Less Discrimination? – A Technical Perspective. Online: https://doi.org/10.30844/wi_2020_q2-schmalenbach (Abgerufen 28.03.24)

Abstract

Abstract

Due to its large cost-saving potential, data-driven recruitment is becoming increasingly popular across various industries. However, several cases were reported where the use of corresponding technologies had caused systematic discrimination against certain candidate groups. While existing approaches to discover and prevent discrimination in data classification mostly perform well within a specific context, it remains unclear to what extent datadriven recruitment can be conducted discrimination-free in real-world business applications, where the respective context-specific assumptions do not necessarily hold. Hence, we first define a generic discrimination model that allows for arbitrary descriptions of job candidate characteristics, before applying two sophisticated discrimination-prevention algorithms on a sample data set generated from our model to evaluate the algorithms’ performance. Our analysis shows that the amount of removed discrimination highly depends on the application context and its underlying definitions and assumptions, making it hard to provide a holistic answer to our research question.

Keywords

Schlüsselwörter

data-driven recruitment, discrimination-aware data mining, discrimination discovery, discrimination prevention

References

Referenzen

1. Berendt, B., Preibusch, S.: Better decision support through exploratory discriminationaware data mining: foundations and empirical evidence. Artif. Intell. Law 22(2), 175–209 (2014)
2. Bogen, M., Rieke, A.: Help wanted: an examination of hiring algorithms, equity. Tech. rep., and bias. Technical report, Upturn (2018)
3. Calders, T., Verwer, S.: Three naive bayes approaches for discrimination-free classification. Data Min. Knowl. Discov. 21(2), 277–292 (2010)
4. Dastin, J.: Amazon scraps secret AI recruiting tool that showed bias against women. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazonscrapssecret- ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G (Accessed:
07.07.2019)
5. Eckhardt, A., Laumer, S., Maier, C., Weitzel, T.: The transformation of people, processes, and it in e-recruiting: Insights from an eight-year case study of a German media corporation. Employee Relations, 36(4), 415–431 (2014)
6. Hajian, S., Domingo-Ferrer, J.: A methodology for direct and indirect discrimination prevention in data mining. IEEE Trans. Knowl. Data Eng. 25(7), 1445–1459 (2013)
7. Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33(1), 1–33 (2011)
8. Kamiran, F., Calders, T., Pechenizkiy, M.: Techniques for discrimination-free predictive models. In: Custers, B., Calders, T., Schermer, B.W., Zarsky, T.Z. (eds.) Discrimination and Privacy in the Information Society – Data Mining and Profiling in Large Databases, Studies in Applied Philosophy, Epistemology and Rational Ethics, vol. 3, pp. 223–239. Springer (2013)
9. Kamiran, F., Zliobaite, I., Calders, T.: Quantifying explainable discrimination and removing illegal discrimination in automated decision making. Knowl. Inf. Syst. 35(3), 613–644 (2013)
10. Laumer, S., von Stetten, A., Eckhardt, A.: E-assessment. Business & Information Systems Engineering, 1(3), 263–265 (2009)
11. Pedreschi, D., Ruggieri, S., Turini, F.: Discrimination-aware data mining. In: Li, Y., Liu, B., Sarawagi, S. (eds.) Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24-27, 2008. pp. 560–568. ACM (2008)
12. Squires, G.D.: Racial profiling, insurance style: Insurance redlining and the uneven development of metropolitan areas. Journal of Urban Affairs 25(4), 391–410 (2003)
13. Thanh, B.L., Ruggieri, S., Turini, F.: k-nn as an implementation of situation testing for discrimination discovery and prevention. In: Apté, C., Ghosh, J., Smyth, P. (eds.) Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 21-24, 2011. pp. 502–510. ACM (2011)
14. Wirtky, T., Laumer, S., Eckhardt, A., Weitzel, T.: On the untapped value of e-HRM: A literature review. Communications of the Association for Information Systems, 38(1) (2016)
15. Zliobaite, I.: Measuring discrimination in algorithmic decision making. Data Min. Knowl. Discov. 31(4), 1060–1089 (2017)
16. Zliobaite, I., Custers, B.: Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models. Artif. Intell. Law 24(2), 183–201 (2016)
17. Zliobaite, I., Kamiran, F., Calders, T.: Handling conditional discrimination. In: Cook, D.J., Pei, J., Wang, W., Zaïane, O.R., Wu, X. (eds.) 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, December 11-14, 2011. pp. 992– 1001. IEEE Computer Society (2011)

Most viewed articles

Meist angesehene Beiträge

GITO events | library.gito