推断与传统人格评估：我们预测的是同一件事吗？
Inferred vs traditional personality assessment: are we predicting the same thing?
摘要：
机器学习方法被研究人员广泛用于从数字记录中预测心理特征。为了找出自动人格评估是否保留了原始特征的属性，我们回顾了最近的220篇文章。首先，我们将来自一个研究子集的预测质量估计放在一起，这些研究声明了训练，验证和测试阶段的分离，这对于确保机器学习中质量估计的正确性至关重要。只有20%的论文符合这一标准。为了比较报告的质量估计值，我们将它们转换为近似的皮尔逊相关系数。预测和自我报告的人格特质之间的相关性的可信上限在0.42和0.48之间变化，这取决于具体的特质。实现的值是大大低于不同的自我报告问卷测量的特质之间的相关性。这表明，我们不能轻易地将人格预测解释为对原始特征的估计，也不能期望预测的人格特征定期重现已知的与生活结果的关系。接下来，我们补充质量估计评估与证据的预测特征的心理测量特性。现有的少数研究结果表明，预测的特质随时间的稳定性较差，有效维度低于自我报告的个性。基于文本的预测模型在其训练域之外的表现要差得多，但仍保持在随机基线之上。预测性状和外部变量之间的关系的证据是混合的。由于缺乏先验假设，预测特征难以用于验证。因此，预测的人格特质无法保留原始特征的重要属性。这就要求谨慎使用和有针对性地验证预测模型。
英文摘要：
Machine learning methods are widely used by researchers to predict psychological characteristics from digital records. To find out whether automatic personality estimates retain the properties of the original traits, we reviewed 220 recent articles. First, we put together the predictive quality estimates from a subset of the studies which declare separation of training, validation, and testing phases, which is critical for ensuring the correctness of quality estimates in machine learning. Only 20% of the reviewed papers met this criterion. To compare the reported quality estimates, we converted them to approximate Pearson correlations. The credible upper limits for correlations between predicted and self-reported personality traits vary in a range between 0.42 and 0.48, depending on the specific trait. The achieved values are substantially below the correlations between traits measured with distinct self-report questionnaires. This suggests that we cannot readily interpret personality predictions as estimates of the original traits or expect predicted personality traits to reproduce known relationships with life outcomes regularly. Next, we complement quality estimates evaluation with evidence on psychometric properties of predicted traits. The few existing results suggest that predicted traits are less stable with time and have lower effective dimensionality than self-reported personality. The predictive text-based models perform substantially worse outside their training domains but stay above a random baseline. The evidence on the relationships between predicted traits and external variables is mixed. Predictive features are difficult to use for validation, due to the lack of prior hypotheses. Thus, predicted personality traits fail to retain important properties of the original characteristics. This calls for the cautious use and targeted validation of the predictive models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
