Surveying Safety-relevant AI Characteristics

Abstract

[Otros] The current analysis in the AI safety literature usually combines a risk or safety issue (e.g., interruptibility) with a particular paradigm for an AI agent (e.g., reinforcement learning). However, there is currently no survey of safety-relevant characteristics of AI systems that may reveal neglected areas of research or suggest to developers what design choices they could make to avoid or minimise certain safety concerns. In this paper, we take a first step towards delivering such a survey, from two angles. The first features AI system characteristics that are already known to be relevant to safety concerns, including internal system characteristics, characteristics relating to the effect of the external environment on the system, and characteristics relating to the effect of the system on the target environment. The second presents a brief survey of a broad range of AI system characteristics that could prove relevant to safety research, including types of interaction, computation, integration, anticipation, supervision, modification, motivation and achievement. This survey enables further work in exploring system characteristics and design choices that affect safety concernsFMP and JHO were supported by the EU (FEDER) and the Spanish MINECO under grant TIN 2015-69175-C4-1-R, by Generalitat Valenciana (GVA) under grant PROME-TEOII/2015/013 and by the U.S. Air Force Office of Scientific Research under award number FA9550-17-1-0287. FMP was also supported by INCIBE (Ayudas para la excelencia de los equipos de investigacion avanzada en ciberseguridad), the European Commission, JRC¿s Centre for Advanced Studies, HUMAINT project (Expert Contract CT-EX2018D335821-101), and UPV PAID-06-18 Ref. SP20180210. JHO was supported by a Salvador de Madariaga grant (PRX17/00467) from the Spanish MECD for a research stay at the Leverhulme Centre for the Future of Intelligence (CFI), Cambridge, and a BEST grant (BEST/2017/045) from GVA for another research stay also at the CFI. JHO and SOH were supported by the Future of Life Institute (FLI) grant RFP2-152. SOH was also supported by the Leverhulme Trust Research Centre Grant RC2015-067 awarded to the Leverhulme Centre for the Future of Intelligence, and a a grant from Templeton World Charity FoundationHernández-Orallo, J.; Martínez-Plumed, F.; Avin, S.; Heigeartaigh, SO. (2019). Surveying Safety-relevant AI Characteristics. CEUR Workshop Proceedings. 1-9. http://hdl.handle.net/10251/146561S1