In this paper we introduce a methodology for identifying early warning signs of transformative progress in AI, to aid anticipatory governance and research prioritisation. We propose using expert elicitation methods to identify milestones in AI progress, followed by collaborative causal mapping to identify key milestones which underpin several others. We call these key milestones ‘canaries’ based on the colloquial phrase ‘canary in a coal mine’ to describe advance warning of an extreme event: in this case, advance warning of transformative AI. After describing and motivating our proposed methodology, we present results from an initial implementation to identify canaries for progress towards high-level machine intelligence (HLMI). We conclude by discussing the limitations of this method, possible future improvements, and how we hope it can be used to improve monitoring of future risks from AI progress.
Awarded Best Paper at the 1st International Workshop on Evaluating Progress in Artificial Intelligence - EPAI 2020 In conjunction with the 24th European Conference on Artificial Intelligence - ECAI 2020 Santiago de Compostela, Spain.