Machine’s Eye View: Postmodern Data Science and the Politics of Ground Truth

This research explores the conceptual and structural intersections between machine learning (ML) and actuarial science, focusing on how both fields construct and govern risk. The core problem addressed is that while modern algorithmic assessment, scoring, and predictive systems rely heavily on actuarial principles, they are rarely analyzed through this historical and epistemic lens. By examining the production of 'ground truth' and the politics of risk governance, the study maps the power-value-knowledge dynamics inherent in these predictive technologies. The paper argues that despite public assertions of mechanical objectivity, the actual practices of both machine learning and actuarial science are fundamentally postmodern, characterized by constructed realities and subjective risk classifications. For M.Tech

researchers, this work provides a theoretical foundation to evaluate algorithmic bias, audit risk-scoring models, and design more transparent data science frameworks. The source paper focuses on a theoretical and sociotechnical analysis; hence, direct empirical datasets or algorithmic implementations are not explicitly detailed, presenting an opportunity for researchers to develop simulation models that quantify the impact of subjective 'ground truth' definitions on classification outcomes.

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