The utilization of informal social networks is an important risk management strategy of vulnerable households in SouthEast Asia. To gain insight on this issue, a social network analysis (SNA) was implemented to assess risk management networks of ethnic minority farm households in the northern uplands of Viet Nam. The results from the analysis suggest that kinship relations and the level of wealth play an essential role in enabling basic network services to function. This paper also points out that effective networks require investments to fulfil the requested mutual obligations and that subsequently, social networks among poor farmers are relatively limited. The findings of the analysis show, not surprisingly, that networks cannot completely buffer severe shocks. Consequently, policy measures to reduce the costs of investing in social capital of poor farmers as well as improved access to appropriate social security systems are essential. These findings are applicable to other upland areas of SouthEast Asia.

Despite the growing availability of data, simulation technologies, and predictive analytics, it is not yet clear whether and under which conditions users will trust Decision Support Systems (DSS). DSS are designed to support users in making more informed decisions in specialized tasks through more accurate predictions and recommendations. This mixed-methods user study contributes to the research on trust calibration by analyzing the potential effects of integrated reliability indication in DSS user interfaces for process management in first-time usage situations characterized by uncertainty. Ten experts specialized in digital tools for construction were asked to test and assess two versions of a DSS in a renovation project scenario. We found that while users stated that they need full access to all information to make their own decisions, reliability indication in DSS tends to make users more willing to make preliminary decisions, with users adapting their confidence and reliance to the indicated reliability. Reliability indication in DSS also increases subjective usefulness and system reliability. Based on these findings, it is recommended that for the design of reliability indication practitioners consider displaying a combination of reliability information at several granularity levels in DSS user interfaces, including visualizations, such as a traffic light system, and to also provide explanations for the reliability information. Further research directions towards achieving trustworthy decision support in complex environments are proposed.

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The case focusses on Rho AI, a data science firm, and its attempt to leverage artificial intelligence to encourage environmental, social and governance investments to limit the impact of climate change. Rho AI’s proposed open-source artificial intelligence tool integrates automated web scraping technology and machine learning with natural language processing. The aim of the tool is to enable investors to evaluate the climate impact of companies and to use this evaluation as a basis for making investments in companies. The case study allows for students to gain an insight into some of the strategic choices that need to be considered when developing an artificial intelligence–based tool. Students will be able to explore the role of ethics in decision-making related to artificial intelligence, while familiarising themselves with key technical terminology and possible business models. The case encourages students to see beyond the technical granularities and to consider the multi-faceted, wider corporate and societal issues and priorities. This case contributes to students recognising that business is not conducted in a vacuum and enhances students’ understanding of the role of business in society during new developments triggered by digital technology.

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