The Human Effect Requires Affect: Addressing Social-Psychological Factors of Climate Change with Machine Learning

TL;DR

This paper explores integrating social-psychological affect into machine learning interventions to improve human engagement and mitigation behaviors related to climate change.

Contribution

It introduces affective agent-based modeling and simulated social dilemmas to incorporate emotional factors into climate change interventions using machine learning.

Findings

Affective factors influence perceptions and willingness to act on climate change.

Affective ML can enhance behavioral and informational interventions.

Potential for wider adoption of mitigative behaviors through emotion-aware strategies.

Abstract

Machine learning has the potential to aid in mitigating the human effects of climate change. Previous applications of machine learning to tackle the human effects in climate change include approaches like informing individuals of their carbon footprint and strategies to reduce it. For these methods to be the most effective they must consider relevant social-psychological factors for each individual. Of social-psychological factors at play in climate change, affect has been previously identified as a key element in perceptions and willingness to engage in mitigative behaviours. In this work, we propose an investigation into how affect could be incorporated to enhance machine learning based interventions for climate change. We propose using affective agent-based modelling for climate change as well as the use of a simulated climate change social dilemma to explore the potential benefits of affective machine learning interventions. Behavioural and informational interventions can be a powerful tool in helping humans adopt mitigative behaviours. We expect that utilizing affective ML can make interventions an even more powerful tool and help mitigative behaviours become widely adopted.