Negotiation in strategy making teams : group support systems and the process of cognitive change

This paper reports on the use of a Group Support System (GSS) to explore at a micro level some of the processes manifested when a group is negotiating strategy-processes of social and psychological negotiation. It is based on data from a series of interventions with senior management teams of three operating companies comprising a multi-national organization, and with a joint meeting subsequently involving all of the previous participants. The meetings were concerned with negotiating a new strategy for the global organization. The research involved the analysis of detailed time series data logs that exist as a result of using a GSS that is a reflection of cognitive theory

Teachers as designers of GBL scenarios: Fostering creativity in the educational settings

This paper presents a research started in 2010 with the aim of fostering the creativity of teachers through the design of Game-Based Learning scenarios. The research has been carried out involving teachers and trainers in the co-design and implementation of digital games as educational resources. Based on the results grained from the research, this paper highlights successful factors of GBL, as well as constraints and boundaries that the introduction of innovative teaching and learning practices faces within educational settings

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019