Forecasting AI Progress: A Research Agenda

TL;DR

This paper develops a research agenda for forecasting AI progress using expert consensus, highlighting key questions, methods, and future directions to improve AI safety and governance planning.

Contribution

It introduces a structured research agenda for AI progress forecasting based on expert opinions, identifying priority questions and promising methods for future work.

Findings

Diverse forecasting methods should be considered.

Validation and action-guiding of forecasts are high priorities.

Statistical methods are promising, but judgmental techniques are also valuable.

Abstract

Forecasting AI progress is essential to reducing uncertainty in order to appropriately plan for research efforts on AI safety and AI governance. While this is generally considered to be an important topic, little work has been conducted on it and there is no published document that gives and objective overview of the field. Moreover, the field is very diverse and there is no published consensus regarding its direction. This paper describes the development of a research agenda for forecasting AI progress which utilized the Delphi technique to elicit and aggregate experts' opinions on what questions and methods to prioritize. The results of the Delphi are presented; the remainder of the paper follow the structure of these results, briefly reviewing relevant literature and suggesting future work for each topic. Experts indicated that a wide variety of methods should be considered for forecasting AI progress. Moreover, experts identified salient questions that were both general and completely unique to the problem of forecasting AI progress. Some of the highest priority topics include the validation of (partially unresolved) forecasts, how to make forecasting action-guiding and the quality of different performance metrics. While statistical methods seem more promising, there is also recognition that supplementing judgmental techniques can be quite beneficial.