Toward a predictive model of moral concern

At the heart of many contentious debates (e.g., on abortion, immigration, or meat consumption, is the question of how much we ought to weigh the welfare and interests of different entities. Previous research has identified numerous characteristics that predict how much concern people show toward different entities, such as empathy or perceived similarity. However, many of these characteristics are correlated, making it difficult to disentangle their unique relation to moral concern, and there is little evidence on the relative importance of different characteristics. We aim to address these issues and move the field toward an integrative model of moral concern. We reviewed the literature to identify hypothesized predictors of moral concern. We will then use a machine learning approach to simultaneously test all identified predictors and build an integrative and parsimonious predictive model (n = 800 U.S. participants). Our findings will provide insights into (1) how accurately we can predict moral concern with the characteristics that were identified in previous research, and (2) which characteristics are most important for predicting moral concern.