洛兰·达斯顿（Lorraine Daston）和彼得·加利森（Peter Galison）指出，“客观性”(objectivity)并非一个固定或普遍的理想，而是一个随着时间演变的概念。在前现代（pre-modern）时期，“符合自然的真理”（truth-to-nature）的客观性注重理想化的呈现，旨在描绘现象的“本质”。19世纪出现了“机械客观性”（mechanical objectivity），强调没有偏见，尽量减少人为干预。近年来，“训练有素的判断”（trained judgment）的客观性承认专家解读的必要性。现代工具（如人工智能）也继续重塑客观性的定义，算法的自动化决策为客观性带来了新的挑战。达斯顿和加利森指出：客观性无法完全消除主观性。即使在追求“机械客观性”的过程中，观察什么、记录或强调什么的选择仍然涉及人为判断。随着科学范式（scientific paradigms）的演变（例如从牛顿力学到量子物理），对于什么构成客观证据或知识的标准也随之变化。
Lorraine Daston and Peter Galison note “objectivity” is not a fixed or universal ideal but rather a concept that has evolved over time. In the pre-modern era, “truth-to-nature” objectivity focused on idealized representations, aiming to depict the “essence” of phenomena. The 19th century saw the rise of “mechanical objectivity”, emphasizing impartiality and minimizing human intervention. More recently, the concept of “trained judgment” acknowledges the necessity of expert interpretation. Modern tools like artificial intelligence continue to reshape the notion of objectivity, as algorithms automate decision-making. Objectivity cannot entirely eliminate subjectivity. Even in the pursuit of mechanical objectivity, decisions about what to observe, record, or emphasize involve human judgment. As scientific paradigms evolve (e.g., from Newtonian mechanics to quantum physics), so too do the standards of what counts as objective evidence or knowledge.
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