TY - GEN
T1 - Which Legal Requirements are Relevant to a Business Process? Comparing AI-Driven Methods as Expert Aid
AU - Sai, Catherine
AU - Sadiq, Shazia
AU - Han, Lei
AU - Demartini, Gianluca
AU - Rinderle-Ma, Stefanie
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Organizations are obliged to ensure compliance with an increasing amount of regulatory requirements stemming from laws, regulations, directives, and policies. As a first step, it is to be determined which of the requirements are relevant in a certain context, depending on factors such as location of the organization and the business processes. For the processes, the identification of relevant requirements can be detailed by an assessment of which parts of each document are relevant for which step of a given process. Nowadays the identification of process-relevant regulatory requirements is mostly done manually by domain and legal experts, posing a tremendous workload due to the extensive number of regulatory documents and their frequent changes. Hence, this work examines how organizations can be assisted in the identification of relevant requirements for their processes based on embedding-based NLP ranking and generative AI. The evaluation highlights strengths and weaknesses of both methods regarding applicability, automation, transparency, and reproducibility. The evaluation results lead to guidelines on which method combinations will maximize benefits for given characteristics such as process usage, impact, and dynamics of an application scenario.
AB - Organizations are obliged to ensure compliance with an increasing amount of regulatory requirements stemming from laws, regulations, directives, and policies. As a first step, it is to be determined which of the requirements are relevant in a certain context, depending on factors such as location of the organization and the business processes. For the processes, the identification of relevant requirements can be detailed by an assessment of which parts of each document are relevant for which step of a given process. Nowadays the identification of process-relevant regulatory requirements is mostly done manually by domain and legal experts, posing a tremendous workload due to the extensive number of regulatory documents and their frequent changes. Hence, this work examines how organizations can be assisted in the identification of relevant requirements for their processes based on embedding-based NLP ranking and generative AI. The evaluation highlights strengths and weaknesses of both methods regarding applicability, automation, transparency, and reproducibility. The evaluation results lead to guidelines on which method combinations will maximize benefits for given characteristics such as process usage, impact, and dynamics of an application scenario.
KW - Business Process Compliance
KW - Large Language Models
KW - Legal Information Retrieval
KW - Regulatory Relevance
UR - http://www.scopus.com/inward/record.url?scp=85193623319&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-59465-6_11
DO - 10.1007/978-3-031-59465-6_11
M3 - Conference contribution
AN - SCOPUS:85193623319
SN - 9783031594649
T3 - Lecture Notes in Business Information Processing
SP - 166
EP - 182
BT - Research Challenges in Information Science - 18th International Conference, RCIS 2024, Proceedings
A2 - Araújo, João
A2 - de la Vara, Jose Luis
A2 - Santos, Maribel Yasmina
A2 - Assar, Saïd
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Research Challenges in Information Science, RCIS 2024
Y2 - 14 May 2024 through 17 May 2024
ER -