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Erschienen in: AI & SOCIETY 4/2017

28.09.2016 | Open Forum

On the promotion of safe and socially beneficial artificial intelligence

verfasst von: Seth D. Baum

Erschienen in: AI & SOCIETY | Ausgabe 4/2017

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Abstract

This paper discusses means for promoting artificial intelligence (AI) that is designed to be safe and beneficial for society (or simply “beneficial AI”). The promotion of beneficial AI is a social challenge because it seeks to motivate AI developers to choose beneficial AI designs. Currently, the AI field is focused mainly on building AIs that are more capable, with little regard to social impacts. Two types of measures are available for encouraging the AI field to shift more toward building beneficial AI. Extrinsic measures impose constraints or incentives on AI researchers to induce them to pursue beneficial AI even if they do not want to. Intrinsic measures encourage AI researchers to want to pursue beneficial AI. Prior research focuses on extrinsic measures, but intrinsic measures are at least as important. Indeed, intrinsic factors can determine the success of extrinsic measures. Efforts to promote beneficial AI must consider intrinsic factors by studying the social psychology of AI research communities.

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Fußnoten
1
For perspectives on near-term AI impacts, see, e.g., Lin et al. (2011). For strong AI, see e.g., Eden et al. (2013).
 
2
On the extrinsic/intrinsic distinction, see e.g., Markowitz and Sharif (2012:246) and references therein.
 
3
See Sotala and Yampolskiy (2014, Sect. 3) for a review in the context of strong AI. Russell et al. (2015) also discuss a range of predominantly extrinsic measures. A notable exception to the focus on extrinsic measures is Russell’s emphasis on shifting “how practitioners define what they do” (Bohannon 2015:252).
 
4
Incentives can nonetheless provoke significant backlash. For example, in the United States, environmentalists have long pursued incentive-based policies such as taxes on pollution in order to appeal to industry interests that do not want constraints, yet industry has been largely successful at avoiding these incentive-based policies.
 
5
Incentives for completed technologies are less relevant for AIs that could be catastrophic because there may be no penalty that could adequately compensate for damages and, in the extreme case, no one alive to process the penalty.
 
6
Conversely, when beneficial AI groups are identified, rewards are to be applied, though this is less of a challenge because AI groups are likely to seek rewards, not dodge them.
 
7
Social sanctions are an extrinsic measure, specifically an incentive using a social penalty, though they can also cultivate certain social norms.
 
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Metadaten
Titel
On the promotion of safe and socially beneficial artificial intelligence
verfasst von
Seth D. Baum
Publikationsdatum
28.09.2016
Verlag
Springer London
Erschienen in
AI & SOCIETY / Ausgabe 4/2017
Print ISSN: 0951-5666
Elektronische ISSN: 1435-5655
DOI
https://doi.org/10.1007/s00146-016-0677-0

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