Knowledge, semantics and AI for risk and crisis management

Abstract

Experience and big amount of data are generated and used in risk and crisis management. Structuring the volume of data and learning from them are still big challenges to be faced to help actors either in decision-making or in operations. Data collection, for instance, is an important aspect, and sometimes, there can be overemphasis on using raw social media data for crisis informatics without adopting appropriate methodologies for cleaning the data and ensuring it is applicable to the situation at hand (i.e. assessing topical relevance). In recent years, this has become even more important with growing recognition that bots can often wield undue influence in social media, especially Twitter. Several techniques have been developed in the last years in Artificial Intelligence (AI) study and Computer Supported Cooperative Work (CSCW) that can be applied to face these challenges. The combination of these tools and methods continue to show promising results in improving sharing of information in crisis and emergency contexts.There are many approaches of AI, such as neural networks and ontologies that can be used to support risk and crisis management.Machine learning, in particular, is an approach that gives "computers the ability to learn without being explicitly programmed" by learning from and making predictions from data. Also, the use of symbolic AI approaches, like ontologies as a knowledge representation mechanism, offers many advantages in information retrieval and analysis.In addition, semantic models of knowledge allow users as well as systems to clearly understand what is happening in a crisis situation and can provide support to decision makers. This special issue mainly addresses the application of semantic models and AI methods and tools trying to answer to users' needs in the scope of risk management, crisis response, prediction, modelling and mitigation. According to a policy forum article in Science in 2016, (Palen & Anderson, 2016) describe crisis informatics as a "multidisciplinary field combining computing and social science knowledge of disasters." The special issue covers a broad range of topics that fall within, and even expand the scope of, crisis informatics. The articles are particularly timely today, as the world grapples with the COVID-19 pandemic that has resulted in hundreds of thousands of deaths, and millions of infections. Although this issue was prepared before the COVID-19 crisis struck, many of the individual topics covered by an international set of authors are highly relevant to the situation unfolding before our eyes.

The development of Kondratieff’s theory of long waves: the place of the AI economy humanization in the ‘competencies-innovations-markets’ model

“…In contrast to Bessen et al (2022), Bobanović (2021), De Nicola et al (2020, Merola (2022), and Pan and Yang (2021), the results obtained in the article indicate that the technocratic approach to the development of the AI economy, which focuses on the development of advanced technologies for the growth of global digital competitiveness and accelerated automation for labor productivity growth, causes high social costs (unemployment, increased social inequality in the form of a digital divide) and therefore increases the risks of socio-economic crises.…”

Sustainable AI in environmental economics and management: Current trends and post-COVID perspective

“…The existing interpretation aims AI at combating crisis phenomena in economic systems; therefore, it is most appropriate to call it anti-crisis AI (Benaben et al, 2020;De Nicola et al, 2020;Wang et al, 2020;Prahl and Goh, 2021;Shakira Fathima and Dilshad Begum, 2021;Hernandez et al, 2022;Simeonovski et al, 2022;Wang, 2022). The concept of "sustainability," in turn, is rooted in environmental protection, to which most of the UN SDGs are devoted (Buonomano et al, 2022;Carayannis et al, 2022;Maheshwari et al, 2022;Úbeda et al, 2022).…”

Essential Features in a Theory of Context for Enabling Artificial General Intelligence

“…Industry-academic cooperation and funding opportunities have been increasing throughout AI, and industry researchers are fairly prominent in the key data mining and machine learning conferences. Another beneficiary of this research will be non-profit and government, especially in topical areas such as disaster relief and crisis management [104]. Building trustworthy AI is an especially important goal if the technology is to witness greater uptake in government agencies and the non-profit sector.…”