Methodology for the Description of Socio-Technical Systems: A Case Study Approach

Area of Science:

• Computer Science
• Sociology
• Ethics
• Regulatory Science

Background:

• Interdisciplinary research often addresses the ethical and regulatory aspects of artificial intelligence (AI) and clinical decision support systems (CDSS).
• Case studies are valuable for preparing AI applications and CDSS for rigorous research and analysis.
• Existing methodologies may lack a standardized approach for categorizing and analyzing socio-technical systems within AI research.

Purpose of the Study:

• To propose a novel procedure model and content categorization framework for socio-technical case studies in AI research.
• To provide a systematic method for evaluating the ethical, social, and regulatory implications of AI and CDSS.
• To facilitate qualitative research and analysis within projects like the DESIREE research project.

Main Methods:

• Development of a procedure model for conducting socio-technical case studies.
• Creation of a categorization system for the content of these case studies.
• Application of the developed methodology to three distinct case studies.

Main Results:

• A validated methodology for socio-technical case study analysis in AI research.
• A structured approach to content categorization, enhancing qualitative research.
• Successful application in the DESIREE project, providing a foundation for ethical, social, and regulatory analyses.

Conclusions:

• The proposed methodology offers a robust framework for socio-technical case studies involving AI and CDSS.
• This approach enhances the systematic evaluation of ethical, social, and regulatory considerations in AI development and deployment.
• The methodology serves as a valuable tool for researchers investigating the societal impact of AI.