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Nous présentons dans cet article les résultats d’une enquête sur les politiques publiques de régulation de l’intelligence artificielle et en particulier sur les stratégies mises en œuvre dans des cadres socio-politiques aux échelles nationales, européennes et internationales. La France a créé des instances dans lesquels des « frottements » entre acteurs différents sont possibles, comme les groupes d’experts ou le Partenariat Mondial sur l’Intelligence Artificielle. Nous considérons que le travail de la part de l’ensemble des groupes sociaux, impliqués dans les instances que nous observons, est consubstantiel à la régulation. Les acteurs publics et privés s’organisent pour échanger et interagir de façon structurée, notamment par la mise en place de ces instances, comme le PMIA. Les dispositifs et instruments, auxquels les différents acteurs participent, contribuent à conférer un sens aux activités de régulation. Notre hypothèse repose sur l’émergence d’un « modèle français » de la régulation qui tend à promouvoir la « confiance » et dont le sens est de parvenir à l’acceptabilité sociale de l’IA.

À seulement dix ans de l’échéance fixée pour atteindre les Objectifs de développement durable (ODD), les agences et organisations de développement ont besoin d’innover rapidement dans leurs approches en matière de processus décisionnels et de résolution des problèmes. De nouveaux enseignements et de nouvelles conceptions, nés des usages émergents des technologies, peuvent servir de vecteurs à certaines innovations. Ce document d’orientation applique le nouveau paradigme des intelligences – qui comprend l’intelligence des données, l’intelligence artificielle, l’intelligence collective et l’intelligence incorporée – pour présenter une vue d’ensemble des risques et bénéfices associés à différents usages émergents des technologies aux praticiens du développement ainsi qu’aux responsables et décideurs politiques. Ces analyses sont, dans la mesure du possible, illustrées d’exemples issus du terrain. Nous recommandons dans ce Policy Paper de créer un cadre décisionnel pour aider les praticiens à déterminer s’ils doivent investir dans des technologies émergentes et comment ces dernières peuvent être efficacement mises au service des objectifs de développement. Cette première itération du cadre décisionnel cherche à définir précisément les objectifs de développement pertinents tout en prenant en compte le contexte existant avant d’aborder la question des solutions en évaluant la maturité, les défis, les implications financières et les risques posés par l’usage des technologies, ainsi que la présence de facteurs limitants et de catalyseurs qui pourraient en moduler l’impact et l’adéquation.

The world is witnessing an unprecedented disruption due to the COVID-19 pandemic in almost all spheres of socio-economic activity. This black swan moment is unprecedented since the advent of the Industrial Revolution. Simultaneously, there is a shift towards effective use of data and technology. There is an exponential increase in the quantum of data collected and has subsequently necessitated a paradigm shift in the functioning of Industry 4.0. Data intelligence, data science, artificial intelligence, machine learning, and deep assimilation of nuanced knowledge revolutionize business and society worldwide. This heralds a potential transition towards data-intensive economies, governments, industrial and social sectors. The accelerated pace of data processing, data intelligence, and analytics encompasses business intelligence, data sciences and machine learning. The spectrum of such upheavals and associated technological transitions is a watershed moment and impacts business and social transformations. In the paper on the role of data in the social realm (Technology as a catalyst for sustainable social business: Advancing the research agenda, 2019). Ashraf et al. opine that “Despite its immense potentials as a sustainable and innovative means to solve specific social problems, the basic concept of the social business model remains unclear to many”. In recent times there has been an inconsistent approach towards social business research. Subsequently, the contemporary business scenario is yet to optimally capitalize on the advantages of the Social Business concept and address the divergent socio-economic and ecological issues worldwide, with profits intact. This should in no way dilute profit maximization for optimizing socio-economic benefits for value creation and sustainability. “Although the social enterprise is often considered to have positive future potential, it is currently underdeveloped” (Bell, 2003). Therefore, social entrepreneurship should generate and ensure nuanced and effective innovations, addressing underserved needs. In contemporary times, tools harnessing Big Data are becoming widely applied, leading to a huge reservoir of untapped diverse data. This subsequently creates an immense opportunity to accelerate the use of Big Data towards social good and sustainability. Though this is a recent trend, it indeed holds promise in the post-COVID world that would be privy to unprecedented socio-economic upheavals and an increased need to address issues of humankind for greater global welfare. In the recent past, diverse data sets have created large-scale solutions in diverse spheres ranging from weather forecasts to airline tickets. Insightful correlations and Big Data go hand in hand and hold the key to several complicated pressing social issues. Therefore, a new crop of social entrepreneurs in public health, social welfare, and humanitarian relief would surely emerge by default. Therefore, it is all the more relevant to make sense of a deluge of Big Data towards alleviating a disease, ecological imbalance, war, and most importantly, the patterns to cope with the disease and its aftermath. This chapter proposes anticipating and predicting the immense possibilities of optimizing Big Data and digitization as key critical drivers of empirical simulation and troubleshooting. Good governance, inclusive society, elimination of corruption, and streamlining policy measures would emerge as default collectives of such social entrepreneurial ventures. The chapter would draw inferences from such models of socially inclined data analytics by data scientists, leading to relevant social models of significance. Implications of the chapter would be to assuage the fault lines, draw inferences from the past, and delve into the plausibility and relevance of Big Data to replicate and innovate socially relevant models to map bigger social issues. This, of course, should have embedded benchmarks of equality, ethics, and empowerment while processing Big Data for a greater social good in a post-COVID world gaping at us.

Social entrepreneurship is today an imperative across the world. The scarcity of resources, the needs of the growing population, and the growing burden on the environment have made people realize a need for organizations that can be profitable while bringing positive change in society and the environment. Sustainable, out-of-the-box solutions are required for solving social challenges. The onus of providing these solutions are often taken by aspiring entrepreneurs, who see opportunity in the challenges and are willing to take risks to create innovative and effective solutions for society’s benefit. The chapter elucidates the meaning of social entrepreneurship, the difference between commercial and social entrepreneurship, the models of social enterprises in practice, and the recommended methodology to evaluate social impact. The chapter features international case studies (through secondary research) and cases about social entrepreneurs who have dramatically improved the lives of people while being financially sustainable – an organization like SELCO Solar Lights Private Ltd, ARMAAN, Yellow Leaf and Arvind Eye Hospital, and many others have provided solutions for the economically weaker section of society. Many of these new-age social entrepreneurs use Big Data and artificial intelligence to ensure that their initiatives create greater social change. Some of these initiatives are highlighted in the chapter.

The establishment of empathy is the premise and foundation for diverse innovative proposals and problem solutions. Virtual reality has provided a full range of depth and breadth for the establishment of empathy in many different types of fields due to its immersive, interactive, and imaginative characteristics. In this study, bibliometric analysis and VOSviewer software are used to cluster and visualize relevant 190 articles from the Web of Science core collection. The essay proposes a positioning of how to apply virtual reality on empathy based on two dimensions, from internal world to external world, and from business innovation to social innovation, by integrating each two of them, four application methods are summarized, which are meaning shaping, value creation, individual satisfaction, and self-realization. What's more, using the bibliometric analysis result as a basis, the application landscape of virtual reality technology for establishing empathy has been constructed, including individual level, society level, and nature level, which reveals the existing and coming possibilities of using VR technology on building empathy in different fields. Last but not least, the paper has discussed the impact of virtual reality for empathy-building from five aspects, economy, politics, culture, society, and ecology. The efforts of this study reveal the VR tendency and have important reference significance for promoting the application of virtual reality technology in creating empathy and innovation in different fields.

Multiplex social network relationships are quite strong in most occurrences, especially within a strong peer network (a cluster of near engaging friends). Moreover, hate speech is found on most online social media platforms. Hence, this study aims to identify hate speech discussions among peer networks. This paper discusses a novel model to recommend a peer under the context of multiplex social networks to minimize the hate speech engagements; Facebook, Twitter, and YouTube social media networks (SMN) were used in this experiment. Collaborative filtering defines an interest-based recommendation model. Under the context of user engagements, some topics become of more user interest. Hence, some social media posts drastically spread over multiplex layers rapidly, initiating a high social impact on a specific topic. The research gap is identifying the peer network that reduces hate speech in multiplex social networks. Hence, this study provides a social innovation platform for peer recommendations to avoid social splits. First, this research contributes by proposing a novel methodology for identifying user engagements on online social networks by mining interactive social network graphs. Secondly, it provides an algorithm for recommending a multi-dimensional recommendation model by using collaborative filtering. Upon the proposed algorithm, a system that recommends engagements in any given online social network to minimize hate speech was implemented. Accordingly, the novel algorithm evaluates by using recommendation precision. The results show that the novel algorithm is highly applicable for peer recommendation in multiplex social networks to avoid hate speech discussions.

Waste generation, especially hazardous waste, can strongly affect the environment and human lives. There is an urgent need to implement sustainable hazardous waste management tools to reduce their harmful impact on the environment stemming from incorrect waste management. However, there is still a lack of business model concepts combining sustainable development and risk management in reverse logistic value chains for hazardous waste. Therefore, the authors develop a novel sustainable business model canvas for both an entity and the logistics system using the Osterwalder's Business Model Canvas integrated with the concept of sustainable development in economic, social and environmental areas (Triple Bottom Line, TBL) and risk-related elements. Then, using the developed sustainable business model canvas, the model for the logistics system for the treatment of hazardous waste containing asbestos was successfully created. The model was implemented in the prototype of computer software in the form of electronic network services.

Human–computer interaction (HCI) is a cornerstone for the success of technical innovation in the logistics and supply chain sector. As a major part of social sustainability, this interaction is changing as artificial intelligence applications (Internet of Things, autonomous transport, Physical Internet) are implemented, leading to larger machine autonomy, and hence the transition from a primary executive to a supervisory role of human operators. A fundamental question concerns the level of control transferred to machines, such as autonomous vehicles and automatic materials handling devices. Problems include a lack of human trust toward automatic decision making or an inclination to override the system in case automated decisions are misperceived. This paper outlines a theoretical framework, describing different levels of acceptance and trust as a key HCI element of technology innovation, and points to the possible danger of an artificial divide at both the individual and firm level. Based upon the findings of four benchmark cases, a classification of the roles of human employees in adopting innovations is developed. Measures at operational, tactical, and strategic level are discussed to improve HCI, more in particular the capacity of individuals and firms to apply state‐of‐the‐art techniques and to prevent an artificial divide, thereby increasing social sustainability.

Cet article est une réponse prospective aux besoins de l'Internet des Objets en termes de simplicité d'utilisation, de gestion de la sécurité et de préservation de la vie privée. Nous proposons de satisfaire ces besoins à travers une plateforme d'intelligence collective utilisant des cartographies sémantiques en réalité augmentées pour récolter les interactions des utilisateurs avec des objets connectés. L'association de l'intelligence collective et des cartographies sémantiques permet d'envisager un design de connaissances où les capacités d'action des objets connectés sont facilement compréhensibles et modifiables par les utilisateurs. Dans ce dispositif, les technologies de blockchain sont utilisées pour partager en sécurité l'expression des utilisateurs et ainsi augmenter la confiance dans l'Internet des Objets et par la même contribuer au développement d'une réflexivité collective sur les usages de ces écosystèmes sociotechniques.

Reputation systems are a popular feature of web-based platforms for ensuring that their users abide by platform rules and regulations and are incentivized to demonstrate honest, trustworthy conduct. Accrual of "reputation" in these platforms, most prominently those in the e-commerce domain, is motivated by self-interested goals such as acquiring an advantage over competing platform users. Therefore, in community-oriented platforms, where the goals are to foster collaboration and cooperation among community members, such reputation systems are inappropriate and indeed contrary to the intended ethos of the community and actions of its members. In this article, we argue for a new form of reputation system that encourages cooperation rather than competition, derived from conceptualizing platform communities as a networked assemblage of users and their created content. In doing so, we use techniques from social network analysis to conceive a form of reputation that represents members' community involvement over a period of time rather than a sum of direct ratings from other members. We describe the design and implementation of our reputation system prototype called "commonshare" and preliminary results of its use within a Digital Social Innovation platform. Further, we discuss its potential to generate insight into other networked communities for their administrators and encourage cooperation between their users.