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Ethics of the Utilization of Artificial Intelligence in Media and Entertainment: Factors that Influence Individuals’ Trust in AI and its Effect on Usage Intention

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Resumo:The emergence of Artificial Intelligence (AI) has led to its inclusion in almost every aspect of our lives, particularly in areas such as medicine, academics, and military. However, two sectors that have been relatively unexplored and where concerns regarding this subject were recently heightened are media and entertainment. This thesis explores the ethical challenges of generative AI usage in the media and entertainment sectors, the factors that influence trust in AI-powered tools among industry professionals and the general public, and the subsequent effect of trust on individuals’ behavioral intention to use these tools. A conceptual model was proposed and empirically tested through Partial Least Squares Structural Equation Modeling (PLS-SEM) using data collected from an online survey. The findings indicate that behavioral intention is primarily influenced by functionality trust, characterized by system-like trusting beliefs (i.e. reliability, functionality, and helpfulness), while being negatively impacted by perceived risk. Functionality trust was mainly shaped by social influence and perceived ease of use and was negatively influenced by perceived risk. Perceived risk, in turn, was primarily dictated by perceived ethicswashing - the belief that media and entertainment companies are misleading in their ethical claims. For individuals within the media or entertainment sectors, social influence had a greater impact on trust, while perceived ethicswashing had a more pronounced effect on perceived risk. This research provides novel insights into the role of generative AI in reshaping the media and entertainment industries, highlighting the importance of genuine ethical practices and the critical factors that foster trust in AI-powered tools.
Autores principais:Santos, Inês Isabel Brandão Barbosa Sequinho dos
Assunto:Generative AI Entertainment Media Ethics Structural Equation Modeling SDG 8 - Decent work and economic growth
Ano:2024
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso embargado
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL
Descrição
Resumo:The emergence of Artificial Intelligence (AI) has led to its inclusion in almost every aspect of our lives, particularly in areas such as medicine, academics, and military. However, two sectors that have been relatively unexplored and where concerns regarding this subject were recently heightened are media and entertainment. This thesis explores the ethical challenges of generative AI usage in the media and entertainment sectors, the factors that influence trust in AI-powered tools among industry professionals and the general public, and the subsequent effect of trust on individuals’ behavioral intention to use these tools. A conceptual model was proposed and empirically tested through Partial Least Squares Structural Equation Modeling (PLS-SEM) using data collected from an online survey. The findings indicate that behavioral intention is primarily influenced by functionality trust, characterized by system-like trusting beliefs (i.e. reliability, functionality, and helpfulness), while being negatively impacted by perceived risk. Functionality trust was mainly shaped by social influence and perceived ease of use and was negatively influenced by perceived risk. Perceived risk, in turn, was primarily dictated by perceived ethicswashing - the belief that media and entertainment companies are misleading in their ethical claims. For individuals within the media or entertainment sectors, social influence had a greater impact on trust, while perceived ethicswashing had a more pronounced effect on perceived risk. This research provides novel insights into the role of generative AI in reshaping the media and entertainment industries, highlighting the importance of genuine ethical practices and the critical factors that foster trust in AI-powered tools.