Artificial Intelligence as a Resilient Tool for Fighting Inequalities in the COVID-19 Crisis
AbstractCan artificial intelligence (AI) be a sustainable way to help solving the Covid-19 global problem? What does the way how welfare states, charity organizations and labour markets are dealing with the pandemic crisis tells us about the AI capacity for reducing exposition of underprivileged groups to the desease? It is becoming more and more visible how the new coronavirus pandemic is affecting specifically the most deprived and vulnerable groups, and also the big difference that welfare states and their policies make. What did the pandemic show about the relations between social inequality, welfare state provision and AI? This presentation will discuss the role of AI as a tool for public policies fighting inequalities that were amplified during the Covid-19 crisis. It will be analysed how the welfare state, the labour market and social communities are already incorporating AI tools and how this can eventually produce more resilient paths. Accelareted and amplified by the Covid-19, several processes of AI penetration in health, education, healthcare, social security, public administrations, labour and surveillance of citizens, became a subject of public discussion. Artificial intelligence is currently a process of long-term change in health and biotechnologies, long-distance education, teleworking, automation, robotization, consumption behaviours, surveillance and human enhancement. An in-deep analysis of the Portuguese case will support the lessons that can be learnt from AI and its use in public policies in a context of pandemic crisis, leading to a set of political recommendations, to promote its application as a resilient tool to fight inequalities.
Aug 15, 2020
How to Cite
CAPUCHA, Luís; NUNES, Nuno; CALADO, Alexandre. Artificial Intelligence as a Resilient Tool for Fighting Inequalities in the COVID-19 Crisis. European Journal of Engineering and Formal Sciences, [S.l.], v. 4, n. 2, p. 10-19, aug. 2020. ISSN 2601-6311. Available at: <http://journals.euser.org/index.php/ejef/article/view/4707>. Date accessed: 29 sep. 2020.