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

By:

Korinek, Anton

Contributor(s):

Material type: Text

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):

Other classification:

Online resources:

Available additional physical forms:

Hardcopy version available to institutional subscribers

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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
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

Total holds: 0

Collection: Colección NBER Close shelf browser (Hides shelf browser)

Previous	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	Next
Previous	nber w30014 Weighted-Average Quantile Regression /	nber w30015 Bunching and Taxing Multidimensional Skills /	nber w30016 The Dynamic Impact of Market Integration: Evidence from Renewable Energy Expansion in Chile /	nber w30017 Aligned with Whom? Direct and Social Goals for AI Systems /	nber w30018 Top Income Inequality and Tax Policy /	nber w30019 Population, Productivity, and Sustainable Consumption /	nber w3002 The Impact of a Ban on Legalized Abortion on Adolescent Childbearing in New York City /	Next

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.