Votre recherche

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.

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.

University students will be our future business leaders, and will have to address social problems caused by business by implementing solutions such as social entrepreneurship ventures. In order to facilitate the learning process that will foster social entrepreneurship, however, a more holistic pedagogy is needed. Based on learning theory, we propose that students' social entrepreneurship actions will depend on their learning about CSR and their absorptive capacity. We propose that instructors and higher education institutions can enhance this absorptive capacity by exploiting Web 2.0 technologies. We tested our proposition with a sample of 425 university students using structural equation modeling and found support for the proposed relationships.

University students will be our future business leaders, and will have to address social problems caused by business by implementing solutions such as social entrepreneurship ventures. In order to facilitate the learning process that will foster social entrepreneurship, however, a more holistic pedagogy is needed. Based on learning theory, we propose that students' social entrepreneurship actions will depend on their learning about CSR and their absorptive capacity. We propose that instructors and higher education institutions can enhance this absorptive capacity by exploiting Web 2.0 technologies. We tested our proposition with a sample of 425 university students using structural equation modeling and found support for the proposed relationships.

The dependence on digital technologies has seen a significant increase during COVID-19 pandemic, to ensure that social connectivity and work goes on, in spite of lockdowns and the physical controls on movement. Though digital learning is expected to create abundant life-long learning opportunities for learners worldwide in this challenging time, there is a danger to further impose inequalities and inadequate access to quality education and life-long learning for the unconnected or poorly connected population. This paper shares our experience of reengineering a MOOC platform as `Community led MOOCs' to serve the learning needs of most under represented single mother communities in Bario - a remote settlement of Kelabits in the Borneo Island of Malaysia. This paper then explores TRIZ based heuristic models to address the socio-technological barriers to lifelong learning and proposes TRIZ principles that can trigger social innovation and creativity in designing lifelong learning solutions for rural communities.

The dependence on digital technologies has seen a significant increase during COVID-19 pandemic, to ensure that social connectivity and work goes on, in spite of lockdowns and the physical controls on movement. Though digital learning is expected to create abundant life-long learning opportunities for learners worldwide in this challenging time, there is a danger to further impose inequalities and inadequate access to quality education and life-long learning for the unconnected or poorly connected population. This paper shares our experience of reengineering a MOOC platform as `Community led MOOCs' to serve the learning needs of most under represented single mother communities in Bario - a remote settlement of Kelabits in the Borneo Island of Malaysia. This paper then explores TRIZ based heuristic models to address the socio-technological barriers to lifelong learning and proposes TRIZ principles that can trigger social innovation and creativity in designing lifelong learning solutions for rural communities.

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.

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.