Skip to main content

Enhancing Business Process Models with Ethical Considerations

  • Conference paper
  • First Online:
Enterprise Design, Operations, and Computing. EDOC 2024 Workshops (EDOC 2024)

Abstract

Fairness has recently emerged as a challenging topic in many areas of computer science, as it is related to algorithms supporting decision-making, experimental research, and information access and processing. As (decision-intensive) business processes are inherently using information to reach their goals, their fairness possibly depends on the kind of information they are allowed to access. In this paper, we deal with this aspect and propose some criteria to consider when conceptually specifying business activities and their related information seamlessly through a recently proposed approach based on the concept of Activity View. More specifically, we distinguish equality and equity as two aspects of fairness and discuss how to enforce them in business process design. Their expression according to the specification of Activity Views is formally proposed and discussed in the paper.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    For sake of simplicity, in the following, we will focus only on the data accessed for the exam management – yellow path.

  2. 2.

    We represent data access operations as a set as the same activity can imply the execution of different queries in many possible orders.

References

  1. Angerschmid, A., Zhou, J., Theuermann, K., Chen, F., Holzinger, A.: Fairness and explanation in AI-informed decision making. Mach. Learn. Knowl. Extr. 4(2), 556–579 (2022)

    Article  MATH  Google Scholar 

  2. Caton, S., Haas, C.: Fairness in machine learning: a survey. ACM Comput. Surv. 56(7), 1–38 (2024)

    Article  MATH  Google Scholar 

  3. Combi, C., Oliboni, B., Weske, M., Zerbato, F.: Conceptual modeling of inter-dependencies between processes and data. In: ACM Symposium on Applied Computing (SAC), pp. 110–119. ACM (2018)

    Google Scholar 

  4. Combi, C., Oliboni, B., Weske, M., Zerbato, F.: Conceptual modeling of processes and data: connecting different perspectives. In: Trujillo, J.C., et al. (eds.) ER 2018. LNCS, vol. 11157, pp. 236–250. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00847-5_18

    Chapter  Google Scholar 

  5. Combi, C., Oliboni, B., Zerbato, F.: Integrated exploration of data-intensive business processes. IEEE Trans. Serv. Comput. 16(1), 383–397 (2023)

    Google Scholar 

  6. Demartini, G., Roitero, K., Mizzaro, S.: Data bias management. Commun. ACM 67(1), 28–32 (2023)

    Article  Google Scholar 

  7. Hutchinson, B., Mitchell, M.: 50 years of test (un)fairness: lessons for machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* 2019, pp. 49–58. Association for Computing Machinery (2019)

    Google Scholar 

  8. Jagadish, H.V., Stoyanovich, J., Howe, B.: The many facets of data equity. ACM J. Data Inf. Qual. 14(4), 27:1–27:21 (2022)

    Google Scholar 

  9. Kasy, M., Abebe, R.: Fairness, equality, and power in algorithmic decision-making. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 576–586. Association for Computing Machinery (2021)

    Google Scholar 

  10. Lee, M.K., Jain, A., Cha, H.J., Ojha, S., Kusbit, D.: Procedural justice in algorithmic fairness: leveraging transparency and outcome control for fair algorithmic mediation. Proc. ACM Hum. Comput. Interact. 3(CSCW), 182:1–182:26 (2019)

    Google Scholar 

  11. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 54(6), 1–35 (2021)

    Article  MATH  Google Scholar 

  12. Narayanan, A.: Translation tutorial: 21 fairness definitions and their politics. In: Proceedings of Conference on Fairness, Accountability, and Transparency, vol. 1170, p. 3 (2018)

    Google Scholar 

  13. Object Management Group. Business Process Model and Notation (BPMN), v2.0.2 (2014). http://www.omg.org/spec/BPMN/2.0.2/

  14. Pujol, D., Machanavajjhala, A.: Equity and privacy: more than just a tradeoff. IEEE Secur. Priv. 19(6), 93–97 (2021)

    Article  MATH  Google Scholar 

  15. Ramadan, Q., Strüber, D., Salnitri, M., Jürjens, J., Riediger, V., Staab, S.: A semi-automated BPMN-based framework for detecting conflicts between security, data-minimization, and fairness requirements. Softw. Syst. Model. 19(5), 1191–1227 (2020). https://doi.org/10.1007/s10270-020-00781-x

    Article  Google Scholar 

  16. Reichert, M.: Process and data: two sides of the same coin? In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7565, pp. 2–19. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33606-5_2

    Chapter  MATH  Google Scholar 

  17. Steen, M.: Ethics as a participatory and iterative process. Commun. ACM 66(5), 27–29 (2023)

    Article  MATH  Google Scholar 

  18. Zehlike, M., Yang, K., Stoyanovich, J.: Fairness in ranking, part II: learning-to-rank and recommender systems. ACM Comput. Surv. 55(6), 1–41 (2022)

    Google Scholar 

  19. Zehlike, M., Yang, K., Stoyanovich, J.: Fairness in ranking, part I: score-based ranking. ACM Comput. Surv. 55(6), 118:1–118:36 (2023)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Beatrice Amico .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Amico, B., Combi, C., Dalla Vecchia, A., Migliorini, S., Oliboni, B., Quintarelli, E. (2025). Enhancing Business Process Models with Ethical Considerations. In: Kaczmarek-Heß, M., Rosenthal, K., Suchánek, M., Da Silva, M.M., Proper, H.A., Schnellmann, M. (eds) Enterprise Design, Operations, and Computing. EDOC 2024 Workshops . EDOC 2024. Lecture Notes in Business Information Processing, vol 537. Springer, Cham. https://doi.org/10.1007/978-3-031-79059-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-79059-1_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-79058-4

  • Online ISBN: 978-3-031-79059-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics