Machine intelligence, systemic risks, and sustainability

Automated decision making and predictive analytics in combination with rapid progress in sensor technology and robotics are likely to change the way individuals, communities, governments and private actors perceive and respond to climate and ecological change. Machine intelligent methods are already today being applied within a number of research fields related to climate change and environmental monitoring. Investments into applications of these technologies in agriculture, forestry and the extraction of marine resources also seem to be increasing rapidly. Here we elaborate the various ways by which machine intelligence is making progress in domains of critical importance for sustainability, with a special emphasis on possible systemic risks. These risks include a) algorithmic bias and allocative harms; b) unequal access and benefits; c) cascading failures and external disruptions; d) mis- and disinformation, and e) trade-offs between efficiency and resilience. We explore these emerging risks and discuss the limitations of current governance mechanisms in addressing the impact of MI risks on sustainability.

Galaz, V. et al. . 2021. Beijer Discussion Paper 274: Machine intelligence, systemic risks, and sustainability. Beijer Discussion Paper Series.