Sage Journals: Discover world-class research

Abstract

The use of algorithmic hiring is on the rise, becoming more and more common due to its capacity to process a large number of applications with efficiency. Despite the advantages of this approach, there have been numerous cases of discriminatory hiring outcomes. A significant contributing factor to such outcomes is the lack of representativeness in the data used to develop these systems. This results in a significant decline in performance for underrepresented groups, disproportionately impacting marginalized communities. Addressing bias in algorithmic hiring requires access to comprehensive datasets that include curriculum vitae (CV) and demographic information reflecting diverse backgrounds. Unfortunately, there is a lack of datasets that serve such a purpose. This paper introduces a data donation campaign designed to collect real-world CVs, including demographic and sensitive information, from a representative sample of individuals in the EU workforce. The paper discusses the design decisions underpinning the campaign, along with the challenges encountered during its deployment and execution. Finally, it offers lessons learned and practical solutions to overcome these challenges, thereby contributing valuable insights for future efforts in this domain.

Keywords

data donation dataset representativeness algorithmic hiring discrimination curriculum vitae

Get full access to this article

View all access options for this article.

References

Ajunwa

Friedler

Scheidegger

C. E.

Venkatasubramanian

(2016). Hiring by algorithm: Predicting and preventing disparate impact. Available at SSRN, 2746078, 29.

Araujo

Wonneberger

Neijens

de Vreese

(2017). How much time do you spend online? Understanding and improving the accuracy of self-reported measures of internet use. Communication Methods and Measures, 11(3), 173–190. https://doi.org/10.1080/19312458.2017.1317337

Arora

(2024). Rainbow operations: Let’s add LGBTQ+ colors to “Doing Good with Good Operations.”. Production and Operations Management, 34(4), 804–811. https://doi.org/10.1177/10591478241239007

Badgett

M. V.

(2009). Best practices for asking questions about sexual orientation on surveys. The Williams Institute, School of Law, The University of California Los Angeles.

Barbierato

Gatti

Incremona

Pozzi

Toti

(2025). Breaking away from AI: The ontological and ethical evolution of machine learning. IEEE Access.

Barocas

Selbst

(2016). Big Data’s Disparate Impact. In 104 California Law Review 671. SSRN.

Baumann

A.-L.

Egenberger

Supik

(2018). Erhebung von Antidiskriminierungsdaten in repräsentativen Wiederholungsbefragungen. Bestandsaufnahme und Entwicklungsmöglichkeiten. Antidiskriminierungsstelle Des Bundes: Berlin, Germany.

Baumgartner

S. E.

Sumter

S. R.

Petkevič

Wiradhany

(2023). A novel iOS data donation approach: Automatic processing, compliance, and reactivity in a longitudinal study. Social Science Computer Review, 41(4), 1456–1472. https://doi.org/10.1177/08944393211071068

Bendick

Jr, M.

Jackson

C. W.

Romero

J. H.

(1997). Employment discrimination against older workers: An experimental study of hiring practices. Journal of Aging & Social Policy, 8(4), 25–46. https://doi.org/10.1300/j031v08n04_03

10.

Bertrand

Mullainathan

(2003). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination (No. w9873). National Bureau of Economic Research. https://doi.org/10.3386/w9873

11.

Bietz

Patrick

Bloss

(2019). Data donation as a model for citizen science health research. Citizen Science: Theory and Practice, 4(1), 6. https://doi.org/10.5334/cstp.178

12.

Boeschoten

Ausloos

Möller

Araujo

T. B.

Oberski

(2020). A framework for digital trace data collection through data donation. https://doi.org/10.5117/CCR2022.2.002.BOES

13.

Boeschoten

de Schipper

N. C.

Mendrik

A. M.

van der Veen

Struminskaya

Janssen

Araujo

(2023). Port: A software tool for digital data donation. The Journal of Open Source Software, 8(90), 5596. https://doi.org/10.21105/joss.05596

14.

Boeschoten

Mendrik

van der Veen

Vloothuis

Voorvaart

Oberski

D. L.

(2022). Privacy-preserving local analysis of digital trace data: A proof-of-concept. Patterns (New York, N.Y.), 3(3), 100444. https://doi.org/10.1016/j.patter.2022.100444

15.

Chander

(2016). The racist algorithm? Mich. L. Rev., 115, 1023.

16.

Chander

(2017). Women of color in the workplace. ENAR’S Ninith European Equal@Work Seminar.

17.

Chen

(2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10(1), 1–12. https://doi.org/10.1057/s41599-023-02079-x

18.

Christner

Urman

Adam

Maier

(2022). Automated tracking approaches for studying online media use: A critical review and recommendations. Communication Methods and Measures, 16(2), 79–95. https://doi.org/10.1080/19312458.2021.1907841

19.

Correll

S. J.

Benard

Paik

(2007). Getting a job: Is there a motherhood penalty? American Journal of Sociology, 112(5), 1297–1338. https://doi.org/10.1086/511799

20.

Couto

Garimella

(2024). Examining (political) content consumption on Facebook through data donation. arXiv preprint arXiv:2407.08171. https://arxiv.org/abs/2407.08171

21.

Daugherty

P. R.

Wilson

H. J.

(2024). Human+ machine, updated and expanded: Reimagining work in the age of AI. Harvard Business Press.

22.

Derous

Ryan

A. M.

(2019). When your resume is (not) turning you down: Modelling ethnic bias in resume screening. Human Resource Management Journal, 29(2), 113–130. https://doi.org/10.1111/1748-8583.12217

23.

EIGE . (2021). Covid-19 derails gender equality gains. European Institute for Gender Equality. https://eige.europa.eu/newsroom/news/covid-19-derails-gender-equality-gains

24.

ENAR . (2016). Towards LGBTIQ equality: Free to be yourself in the EU. European Network Against Racism. https://www.enar-eu.org/wp-content/uploads/edc-general_factsheet_final.pdf

25.

European Commission . (2020). Towards lgbtiq equality: Free to be yourself in the EU. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0698

26.

European Union . (2019). Discrimination in the European Union. Publications Office of the European Union.

27.

Eurostat . (2023). Migration and asylum in Europe – 2023 edition.

28.

Fabris

Baranowska

Dennis

M. J.

Hacker

Saldivar

Borgesius

F. Z.

Biega

A. J.

(2025). Fairness and Bias in algorithmic hiring: A multidisciplinary survey. ACM Transactions on Intelligent Systems and Technology, 16(1), 1–54. https://doi.org/10.1145/3696457

29.

Finck

Pallas

(2020). They who must not be identified—distinguishing personal from non-personal data under the GDPR. International Data Privacy Law, 10(1).

30.

Fuller

J. B.

Raman

Sage-Gavin

Hines

(2021). Hidden workers: Untapped talent. Harvard Business School Project on Managing the Future of Work and Accenture, 1. https://www.hbs.edu/managing-the-future-of-work/Documents/research/hiddenworkers09032021.pdf

31.

Garimella

Chauchard

(2024). WhatsApp explorer: A data donation tool to facilitate research on WhatsApp. https://arxiv.org/abs/2404.01328

32.

Hummel

Braun

Dabrock

(2019). Data donations as exercises of sovereignty. The Ethics of Medical Data Donation, 137, 23–54.

33.

Keusch

Pankowska

P. K.

Cernat

Bach

R. L.

(2024). Do you have two minutes to talk about your data? Willingness to participate and nonparticipation bias in Facebook data donation. Field Methods, 36(4), 279–293. https://doi.org/10.1177/1525822X231225907

34.

Kleinberg

Mullainathan

Raghavan

(2016). Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807.

35.

Köchling

Wehner

M. C.

(2020). Discriminated by an algorithm: A systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, 13(3), 795–848. https://doi.org/10.1007/s40685-020-00134-w

36.

Kohne

Montag

(2024). ChatDashboard: A framework to collect, link, and process donated WhatsApp chat log data. Behavior Research Methods, 56(4), 3658–3684. https://doi.org/10.3758/s13428-023-02276-1

37.

Kovacheva

Popivanov

Peeva

Coletto

Dimitriadis

Fullin

Fischer-Souan

Marcovici

(2018). Growth, equal opportunities, migration and markets (GEMM) report on institutional and contextual factors.

38.

Kuiper

(2021). Developing a user interface design to foster trust and maximize consent for data donation in scientific research. (Master Thesis). https://studenttheses.uu.nl/handle/20.500.12932/293

39.

Maus

Salvi

Olsson

(2020). Enhancing citizens trust in technologies for data donation in clinical research: Validation of a design prototype. In Companion Proceedings of the 10th International Conference on the Internet of Things. Association for Computing Machinery.

40.

Ohme

Araujo

(2022). Digital data donations: A quest for best practices. Patterns, 3(4), Article 100467. https://doi.org/10.1016/j.patter.2022.100467

41.

Ohme

Araujo

Boeschoten

Freelon

Ram

Reeves

B. B.

Robinson

T. N.

(2024). Digital trace data collection for social media effects research: APIs, data donation, and (screen) tracking. Communication Methods and Measures, 18(2), 124–141. https://doi.org/10.1080/19312458.2023.2181319

42.

Ohme

Araujo

de Vreese

C. H.

Piotrowski

J. T.

(2020). Mobile data donations: Assessing self-report accuracy and sample biases with the iOS screen time function. Mobile Media & Communication, 9(2), 293–313. https://doi.org/10.1177/2050157920959106

43.

Ortega

A. G.

Bourgeois

Kortuem

(2021). Towards designerly data donation. In UbiComp/ISWC 2021 - Adjunct proceedings of the 2021 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2021 ACM international symposium on wearable computers (pp. 496–501). Association for Computing Machinery.

44.

Pager

Western

(2012). Identifying discrimination at work: The use of field experiments. Journal of Social Issues, 68(2), 221–237. https://doi.org/10.1111/j.1540-4560.2012.01746.x

45.

Palmer

Harley

(2012). Models and measurement in disability: An international review. Health Policy and Planning, 27(5), 357–364. https://doi.org/10.1093/heapol/czr047

46.

Parasurama

Sedoc

(2021). Degendering resumes for fair algorithmic resume screening. arXiv preprint arXiv:2112.08910. https://doi.org/10.48550/arXiv.2112.08910

47.

Parasurama

Sedoc

Ghose

(2022). Gendered information in resumes and hiring bias: A predictive modeling approach. Available at SSRN 4074976.

48.

Pfiffner

(2024). Data donation as an approach to studying digital media use–exploring opportunities, challenges, and applications. Doctoral dissertation. University of Zurich.

49.

Puschmann

(2019). Beyond the bubble: Assessing the diversity of political search results. Digital Journalism, 7(6), 824–843. https://doi.org/10.1080/21670811.2018.1539626

50.

Raghavan

Barocas

Kleinberg

Levy

(2020). January). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 469–481). Association for Computing Machinery.

51.

Rosales

Buslón

Curi

Jorge

(2024). Beyond the algorithm: Expanding the understanding of fairness and non-discrimination in algorithmic hiring – The case for Latin American migrants seeking employment opportunities in Spain. AI Advisory Boards. https://aiadvisoryboards.wordpress.com/2024/01/27/beyond-the-algorithm-expanding-the-understanding-of-fairness-and-non-discrimination-in-algorithmic-hiring-the-case-for-latin-american-migrants-seeking-employment-opportunities-in-spain/

52.

Silber

Breuer

Felderer

Gerdon

Stammann

Daikeler

Keusch

Weiß

(2024). Asking for traces: A vignette study on acceptability norms and personal willingness to donate digital trace data. In Social Science Computer Review. Sage Journals. https://doi.org/10.31235/osf.io/2aum8

53.

Skatova

Goulding

(2019). Psychology of personal data donation. PLoS One, 14(11), Article e0224240. https://doi.org/10.1371/journal.pone.0224240

54.

Skatova

Shiells

Boyd

(2019). Attitudes towards transactional data donation and linkage in a longitudinal population study: Evidence from the Avon longitudinal study of parents and children. Wellcome Open Research, 4, 192. https://doi.org/10.12688/wellcomeopenres.15557.2

55.

Strotbaum

Pobiruchin

Schreiweis

Wiesner

Strahwald

(2019). Your data is gold – Data donation for better healthcare? It - Information Technology, 61(5–6), 219–229. https://doi.org/10.1515/itit-2019-0024

56.

Suresh

Guttag

J. V.

(2019). A framework for understanding unintended consequences of machine learning. arXiv preprint arXiv:1901.10002, 2(8), 73.

57.

Tilmes

(2022). Disability, fairness, and algorithmic bias in AI recruitment. Ethics and Information Technology, 24(2), 1–13. https://doi.org/10.1007/s10676-022-09633-2

58.

United Nations Department of Economic and Social Affairs (2024). World Population Prospects 2024. In Statistical Papers - United Nations (Ser. A), Population and Vital Statistics Report. UN-iLibrary.

59.

van Bekkum

Zuiderveen

(2023). Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception? In Computer Law & Security Review (48). ScienceDirect.

60.

van Driel

I. I.

Giachanou

Loes Pouwels

Boeschoten

Beyens

Valkenburg

P. M.

(2022). Promises and pitfalls of social media data donations. Communication Methods and Measures. https://doi.org/10.1080/19312458.2022.2109608

61.

Weitzman . (2010). Sharing medical data for health research: The early personal health record experience. Journal of Medical Internet Research, 12(2). https://doi.org/10.2196/jmir.1356

62.

Yam

Skorburg

J. A.

(2021). From human resources to human rights: Impact assessments for hiring algorithms. Ethics and Information Technology, 23(4), 611–623. https://doi.org/10.1007/s10676-021-09599-7

63.

Zannettou

Nemes-Nemeth

Ayalon

Goetzen

Gummadi

K. P.

Redmiles

E. M.

Roesner

(2024). Analyzing user engagement with TikTok’s short format video recommendations using data donations. In Proceedings of the 2024 CHI conference on human factors in computing systems (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642433

Data Donation: A First Step Towards Improving Representativeness in Algorithmic Hiring Datasets

Abstract

Keywords

Get full access to this article

References