Social Implications of Socially-Guided Machine Learning for Innovation Support

Automated decision support might be insufficient for managing wicked problems; instead, new social arrangements might be required to alter the trajectory of a social system. Since socio-technical systems will comprise human and computational intelligences interacting, we can leverage that interaction by using Artificial Intelligence (AI) to build innovation-support systems (ISS), which interleaves human expertise, generative AI and multi-agent simulation to imagine alternative social arrangements and envision societal trajectories. We define a socially-guided machine-learning framework that provides a ‘safe space’ for exploration and an evidence-base for selecting and modifying social arrangements. The significance of socially guided machine learning for innovation support is to assist societies with mitigating wicked problems through co-production with AI.