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