From service design thinking to the third generation of activity theory: a new model for designing AI-based decision-support systems - Marocco, Silvia; Talamo, Alessandra; Quintiliani, Francesca (Frontiers in Artificial Intelligence, 2024)
The rise of Artificial Intelligence (AI), particularly machine learning, has brought a significant transformation in decision-making (DM) processes within organizations, with AI gradually assuming responsibilities that were traditionally performed by humans. However, as shown by recent findings, the acceptance of AI-based solutions in DM remains a concern as individuals still strongly prefer human intervention. This resistance can be attributed to psychological factors and other trust-related issues. To address these challenges, recent studies show that practical guidelines for user-centered design of AI are needed to promote justified trust in AIbased systems. To this aim, our study bridges Service Design Thinking and the third generation of Activity Theory to create a model which serves as a set of practical guidelines for the user centered design of Multi-Actor AI-based DSS. This model is created through the qualitative study of human activity as a unit of analysis. Nevertheless, it holds the potential for further enhancement through the application of quantitative methods to explore its diverse dimensions more extensively. As a result, the qualitative methodology employed in our study can be characterized as a "pre-quantitative" investigation. This framework aims at locating the contribution of AI in complex human activity and identifying the potential role of quantitative data in it. As an illustrative example, we used a case study in the field of human capital investments, with a particular focus on organizational development, which involves managers, professionals, coaches and other significant actors.