English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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