dc.contributor.author
Gruetzemacher, Ross
dc.contributor.author
Dorner, Florian E.
dc.contributor.author
Bernaola-Alvarez, Niko
dc.contributor.author
Giattino, Charlie
dc.contributor.author
Manheim, David
dc.date.accessioned
2021-11-01T13:13:35Z
dc.date.available
2021-11-01T13:13:35Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/32454
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-32179
dc.description.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 a balanced 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. 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 forecasts 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.
en
dc.format.extent
20 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc
500 Naturwissenschaften und Mathematik::510 Mathematik::510 Mathematik
dc.title
Forecasting AI progress: A research agenda
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
120909
dcterms.bibliographicCitation.doi
10.1016/j.techfore.2021.120909
dcterms.bibliographicCitation.journaltitle
Technological Forecasting and Social Change
dcterms.bibliographicCitation.volume
170
dcterms.bibliographicCitation.url
https://doi.org/10.1016/j.techfore.2021.120909
refubium.affiliation
Mathematik und Informatik
refubium.affiliation.other
Institut für Mathematik
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refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
1873-5509
refubium.resourceType.provider
WoS-Alert