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Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

Based on an extensive literature review, this chapter outlines key developments in global health and research during the last century with focus on the emergence of violence and child maltreatment as international public health priorities. Violence has been known to humans for millennia, but only in the late 1990s was it recognised as a global public health issue. Every year, an estimated 1 billion children are exposed to trauma, loss, abuse and neglect. Child maltreatment takes a social and economic toll on countries. Research initiated in 1985 found child maltreatment to be associated with increased disease, disability and premature death in adult survivors. The global availability of data on child maltreatment is, however, sporadic with low validity and reliability. Few global experts have consulted and involved the survivors of child maltreatment, as the experts by experience, in their attempts to provide a more comprehensive picture of reality. Youth and adult survivors of child maltreatment are often traumatised by the experience, and it is important to use trauma-informed approaches to prevent re-traumatisation. Participatory and inclusive research on child maltreatment is only in its infancy. There is a need for more inclusive research, designed by survivors for survivors, hereby strengthening local capacity building and informing policymakers from the bottom up. This chapter reviews lessons learnt and provides recommendations for how to enhance the participation and inclusion of the experts by experience in research on child maltreatment.

The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?

This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.

Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.