A Machine-Learning Analysis of the Impacts of the COVID-19 Pandemic on Small Business Owners and Implications for Canadian Government Policy Response.

This study applies a machine-learning technique to a dataset of 38,000 textual comments from Canadian small business owners on the impacts of coronavirus disease 2019 (COVID-19). Topic modelling revealed seven topics covering the short- and longer-term impacts of the pandemic, government relief programs and loan eligibility issues, mental health, and other impacts on business owners. The results emphasize the importance of policy response in aiding small business crisis management and offer implications for theory and policy. Moreover, the study provides an example of using a machine-learning-based automated content analysis in the fields of crisis management, small business, and public policy.

Cette étude applique une technique d'apprentissage automatique à un ensemble de données de 38 000 commentaires publiés par des propriétaires de petites entreprises canadiennes sur les impacts de la maladie à coronavirus 2019 (COVID-19). La modélisation thématique a révélé sept sujets couvrant les effets de la pandémie à court et à long terme, les programmes d'aide gouvernementaux, les questions d'admissibilité aux prêts, la santé mentale ainsi que d'autres retombées sur les propriétaires d'entreprise. Les résultats soulignent l'importance d'une intervention politique pour aider les petites entreprises à gérer la crise et offrent des implications pour la théorie et la politique. En outre, l'étude fournit un exemple d'utilisation d'une analyse automatisée de contenu basée sur l'apprentissage automatique dans les domaines de la gestion de crise, des petites entreprises et de la politique publique.

A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation

The scholarly literature on the links between Artificial Intelligence and the United Nations’ Sustainable Development Goals is burgeoning as climate change and the biotic crisis leading to mass extinction of species are raising concerns across the globe. With a focus on Sustainable Development Goals 14 (Life below Water) and 15 (Life on Land), this paper explores the opportunities of Artificial Intelligence applications in various domains of wildlife, ocean and land conservation. For this purpose, we develop a conceptual framework on the basis of a comprehensive review of the literature and examples of Artificial Intelligence-based approaches to protect endangered species, monitor and predict animal behavior patterns, and track illegal or unsustainable wildlife trade. Our findings provide scholars, governments, environmental organizations, and entrepreneurs with a much-needed taxonomy and real-life examples of Artificial Intelligence opportunities for tackling the grand challenge of rapidly decreasing biological diversity, which has severe implications for global food security, nature, and humanity.

The Acceptance of Digital Surveillance in an Age of Big Data

News media companies and human rights organizations have been increasingly warning about the rise of the surveillance state that builds on distrust and mass surveillance of its citizens. The COVID-19 pandemic is fostering digitalization and state-corporate collaboration, leading to the introduction of contact tracing apps and other digital surveillance technologies that bring about societal benefits, but also increase privacy invasion. This study examines citizens' concerns about their digital identity, the nation-state's intelligence activities, and the security of biodata, addressing their impacts on the trust in and acceptance of governmental use of personal data. Our analysis of survey data from 1,486 Canadians suggest that those concerns have negative impacts on citizens' acceptance of governmental use of personal data, but not necessarily on their trust in the nation-state being respectful of privacy. Government and corporations, it is concluded, should be more transparent about the collection and uses data, and citizens should be more active in "watching the watchers" in the age of Big Data.