ABSTRACT
This chapter explores the critical role of content analysis in the development of artificial intelligence, emphasizing the importance of precise, impartial data labeling to mitigate biases and enhance algorithmic fairness. It traces the historical evolution of content analysis, from its origins in print media scrutiny to its contemporary application in digital media, highlighting the methodological shifts from quantitative to qualitative approaches. The chapter discusses the challenges and implications of manual and automated data annotation, underscoring the necessity for robust sampling and the potential pitfalls of relying solely on algorithmic interpretations in contexts like contextual advertising and societal norm constructions.
