Black Boxes and the Role of Modeling in Environmental Policy Making

Abstract

Modeling is essential for modern science, and science-based policies are directly affected by the reliability of model outputs. Artificial intelligence has improved the accuracy and capability of model simulations, but often at the expense of a rational understanding of the systems involved. The lack of transparency in black box models, artificial intelligence based ones among them, can potentially affect the trust in science driven policy making. Here, we suggest that a broader discussion is needed to address the implications of black box approaches on the reliability of scientific advice used for policy making. We argue that participatory methods can bridge the gap between increasingly complex scientific methods and the people affected by their interpretations

Atmospheric N Deposition Significantly Enhanced Soil N2O Emission From Eastern China Forests

“…Predicting the soil N 2 O emission rate ( R N2O ) is challenging because of the incomplete understanding of the mechanisms and the limited ability to capture the spatiotemporal variation of R N2O itself or the underlying factors. In this study, we used a “gray box” conceptual model (Maeda et al., 2021; Text S1 in Supporting Information S1) to deduce the relationship between N depo and R N2O on large spatiotemporal scales (Figure 1).…”

Machine learning partners in criminal networks

“…Unfortunately, and as also occurs in many other empirical works with social systems, noisy data and missing information are more a rule than an exception. Another limitation is the lack of straightforward interpretations of machine learning methods and the consequent difficulty in deriving causal relationships from these models [38][39][40] . Fortunately, there is a growing consensus that, in addition to delivering high prediction accuracy, machine learning methods must also be capable of producing knowledge from data, a domain that is referred to as "interpretable machine learning" and that is experiencing rapid developments 41 , particularly in the context of graph representation learning 42,43 .…”

Automatically Generating Scenarios from a Text Corpus: A Case Study on Electric Vehicles

“…Indeed, the socio-environmental systems examined in sustainability studies are often complex, and models are at risk of becoming a 'black box' by being almost as complex. Maeda and colleagues stressed that "as the increasing complexity of models starts to influence policy making, it is important for scien-tists to create new approaches to communicate their underlying assumptions, reasoning, data and methods to stakeholders" [102]. Future work can thus contribute further to this communication component, for instance, by leveraging the Q&A system not only to build the model but also to ask how conclusions were reached.…”