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This academic article delves into the complexities of big data and artificial intelligence (ai), challenging the prevailing myths and hype surrounding these technologies. It argues that the widespread adoption of data-driven approaches in various sectors, from business to government, has led to an overestimation of their capabilities and a neglect of their limitations. The authors advocate for a more critical and reflexive approach to data science and machine learning, emphasizing the need to understand the social, historical, and ethical contexts in which these technologies are developed and deployed. They propose a framework for situating methods within the broader context of knowledge production, urging practitioners to prioritize reflection and recognize the situated nature of all knowledge.
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