Robotic Process Automation (RPA) is a maturing technology that sits between the fields of Business Process Management (BPM) and Artificial Intelligence (AI). RPA allows organizations to automate highvolume and repetitive tasks performed by human operators. These tasks are enacted using a software (SW) robot that works on the applications' user interfaces (UIs) as the original human operators did. The current generation of RPA tools is driven by predefined rules and manual configurations made by expert users rather than intelligent solutions, making the current practice time-consuming and error-prone. In this talk, we focus on a recent line of research devoted to leveraging the combined use of process mining and reasoning about actions in AI to evolve RPA from a mere automated technology to a (framed) autonomous solution capable of complex decision-making activities. In this journey, we also conceptualize the notion of trust between humans and SW robots by discussing the research challenges to pioneer new trust-aware solutions that work in partnership with the human workforce and strike the right balance of autonomy and trust for achieving intelligent RPA.
CEUR
ceur-ws.org [11], automated planning [12] and process mining [13, 14] were proposed to inject intelligence into current RPA technology.
In an era where RPA is pushing the automation of human tasks to the extreme, on the other hand, recent research studies conducted on the efectiveness of RPA within organizations https://www.diag.uniroma1.it/marrella/ (A. Marrella)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). have found that implementation of SW robots does not always lead to the assumed efect, and many SW robots are subsequently withdrawn. Consequently, the human workforce takes over robotized tasks to perform them manually again and, in practice, replaces SW robots, leading to a costly remanualization of the respective task [15]. One frequently cited barrier to wider RPA adoption is the lack of trust between humans and SW robots [16, 17, 18]. Since human employees are expected to share responsibilities with the SW robots, trust in their performance is crucial for ensuring this technology’s adoption and proper use.
Although the literature on human-AI collaboration has extensively explored trust issues, ofering valuable lessons for RPA [ 19], the development of a framework striking a balance between providing autonomy and trust for RPA requires considering the transactional, nonanthropomorphic and abstract nature of SW robots, which is a specific nuance of this technology. That is, the end-user perception of trust in RPA strongly depends on the outcomes the SW robots deliver as the result of task execution.
In this talk, after discussing a recent line of research devoted to leveraging the combined use of process mining and reasoning about actions in AI to evolve RPA from an automated technology to a (framed) autonomous solution, we report on the key insights of a Dagstuhl Seminar organized in July 2024, entitled Improving Trust between Humans and Software Robots in Robotic Process Automation.1 The seminar was organized to pioneer new intelligent trust-aware RPA solutions that work in partnership with the human workforce. Specifically, we present the key factors contributing to creating or eroding trust in RPA and consolidate them in a conceptual framework that indicates the dimensions and characteristics of trust. Then, we specify the notion of trust in RPA as a measurable construct – Willingness to Give Up Control (WGUC) – that allows assessing the level of trust between humans and SW robots. Finally, we present the significant research challenges in the transition toward trustworthy and intelligent RPA, and chart a roadmap for future RPA research.
Acknowledgments. This work has been supported by the Sapienza project FOND-AIBPM and the PNRR MUR project PE0000013-FAIR. The author would like to thank all the people involved in the Dagstuhl Seminar Improving Trust between Humans and Software Robots in Robotic Process Automation for their ideas and lively discussions, which have contributed significantly to the content of this talk, and in particular: Simone Agostinelli, Marco Angelini, Aleksandre Asatiani, Bernhard Axmann, Piercosma Bisconti, Angelo Casciani, Christian Czarnecki, Adela del Río Ortega, Andrea Delgado, José González Enríquez, Glenda Hannibal, Christian Janiesch, Andrés Jiménez Ramírez, Faizan Ahmed Khan, Antonio Martínez Rojas, Artur Modlinski, Ralf Plattfaut, Jana-Rebecca Rehse, Hajo A. Reijers, Manuel Resinas, Michael Rosemann, Flávia Santoro, Stefan Sarkadi, Pnina Sofer, Barbara Weber, and Adriana Wilde. [3] A. Villa, S. Ray, M. Alexander, S. Joshi, M. Helsel, 2024 Magic Quadrant Report for RPA, 2024. URL: https://www.gartner.com/en/documents/5656223. [4] A. Jimenez-Ramirez, H. A. Reijers, I. Barba, C. Del Valle, A Method to Improve the Early Stages of the Robotic Process Automation Lifecycle, in: 31st Int. Conf. on Advanced Information Systems Engineering (CAiSE 2019), 2019, pp. 446–461. [5] S. Agostinelli, A. Marrella, M. Mecella, Research Challenges for Intelligent Robotic Process
Automation, in: Business Process Management Workshops, 2019, pp. 12–18. [6] T. Chakraborti, V. Isahagian, R. Khalaf, Y. Khazaeni, V. Muthusamy, Y. Rizk, M. Unuvar, From Robotic Process Automation to Intelligent Process Automation, in: Business Process Management: Blockchain and Robotic Process Automation Forum, 2020, pp. 215–228. [7] S. Agostinelli, A. Marrella, M. Mecella, Towards Intelligent Robotic Process Automation for BPMers, CoRR abs/2001.00804 (2020). URL: http://arxiv.org/abs/2001.00804. [8] A. Martínez-Rojas, A. Rodríguez-Ruíz, J. G. Enríquez, A. J. Ramirez, What’s Behind the Screen? Unveiling UI Hierarchies in Process-Related UI Logs, in: 22nd Int. Conf. on Business Process Management (BPM 2024), 2024, pp. 256–272. [9] L. Laakmann, S. A. Ciftci, C. Janiesch, A Nascent Taxonomy of Machine Learning in Intelligent Robotic Process Automation, in: Business Process Management Forum, 2024, pp. 319–336. [10] H. van der Aa, H. Leopold, Supporting RPA through Natural Language Processing, Robotic
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