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Empowering Ethical Insights among Data Scientists
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Resumo(s)
This thesis explores the ethical challenges faced by data scientists and artificial intelligence (AI) practitioners in navigating complex ethical dilemmas, with a focus on enhancing ethical decision-making capabilities. The research employs a systematic literature review methodology, analyzing key studies across diverse domains, including healthcare, education, and big data management. The findings reveal a universal consensus on the importance of integrating ethical considerations into data science and AI practices, emphasizing the need for
domain-specific guidelines and practical methodologies, through thematic categorization, this work identifies critical areas: the role of education in fostering ethical awareness, challenges in operationalizing theoretical frameworks, and the importance of continuous ethical reflection throughout the data science lifecycle, the thesis highlights innovative solutions, including participatory design processes and adaptable ethical decision-making frameworks, while addressing gaps in stakeholder engagement and cross-sector consistency, this study
contributes to the discourse by bridging the gap between theoretical ethics and practical implementation, providing actionable insights for policymakers, educators, and practitioners. It underscores the necessity of interdisciplinary collaboration to ensure that technological advancements align with ethical principles and societal values, paving the way for a more responsible and inclusive data-driven future.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing Intelligence
Palavras-chave
Ethical challenges in data science AI ethics Data-driven decision-making Ethical innovation Societal values in AI SDG 4 - Quality education SDG 9 - Industry, innovation and infrastructure SDG 10 - Reduced inequalities SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals