Aligned with Whom? Direct and Social Goals for AI Systems / Anton Korinek, Avital Balwit.

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Korinek, Anton

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TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w30017.Publication details: Cambridge, Mass. National Bureau of Economic Research 2022.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: As artificial intelligence (AI) becomes more powerful and widespread, the AI alignment problem--how to ensure that AI systems pursue the goals that we want them to pursue--has garnered growing attention. This article distinguishes two types of alignment problems depending on whose goals we consider, and analyzes the different solutions necessitated by each. The direct alignment problem considers whether an AI system accomplishes the goals of the entity operating it. In contrast, the social alignment problem considers the effects of an AI system on larger groups or on society more broadly. In particular, it also considers whether the system imposes externalities on others. Whereas solutions to the direct alignment problem center around more robust implementation, social alignment problems typically arise because of conflicts between individual and group-level goals, elevating the importance of AI governance to mediate such conflicts. Addressing the social alignment problem requires both enforcing existing norms on their developers and operators and designing new norms that apply directly to AI systems.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w30017 (Browse shelf(Opens below))	Not for loan

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May 2022.

As artificial intelligence (AI) becomes more powerful and widespread, the AI alignment problem--how to ensure that AI systems pursue the goals that we want them to pursue--has garnered growing attention. This article distinguishes two types of alignment problems depending on whose goals we consider, and analyzes the different solutions necessitated by each. The direct alignment problem considers whether an AI system accomplishes the goals of the entity operating it. In contrast, the social alignment problem considers the effects of an AI system on larger groups or on society more broadly. In particular, it also considers whether the system imposes externalities on others. Whereas solutions to the direct alignment problem center around more robust implementation, social alignment problems typically arise because of conflicts between individual and group-level goals, elevating the importance of AI governance to mediate such conflicts. Addressing the social alignment problem requires both enforcing existing norms on their developers and operators and designing new norms that apply directly to AI systems.