Operationalizing Contextual Integrity in Privacy-Conscious Assistants
Advanced AI assistants combine frontier LLMs and tool access to autonomously
perform complex tasks on behalf of users. While the helpfulness of such
assistants can increase dramatically with access to user information including
emails and documents, this raises privacy concerns about assistants sharing
inappropriate information with third parties without user supervision. To steer
information-sharing assistants to behave in accordance with privacy
expectations, we propose to operationalize contextual integrity
(CI), a framework that equates privacy with the appropriate flow of information
in a given context. In particular, we design and evaluate a number of
strategies to steer assistants' information-sharing actions to be CI compliant.
Our evaluation is based on a novel form filling benchmark composed of synthetic
data and human annotations, and it reveals that prompting frontier LLMs to
perform CI-based reasoning yields strong results.
66b1e42e874d8abe0b6e5963
Sahra Ghalebikesabi
admin_assigned
2024-08-06T11:55:07.372Z
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Sahra Ghalebikesabi
user
sahra
66b1e42e874d8abe0b6e5964
Eugene Bagdasaryan
admin_assigned
2024-08-06T11:55:14.976Z
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Eugene Bagdasaryan
user
ebagdasa
66b1e42e874d8abe0b6e5965
Ren Yi
66b1e42e874d8abe0b6e5966
Itay Yona
admin_assigned
2024-08-06T11:55:27.838Z
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Itay Yona
user
tux
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Ilia Shumailov
66b1e42e874d8abe0b6e5968
Aneesh Pappu
66b1e42e874d8abe0b6e5969
Chongyang Shi
66b1e42e874d8abe0b6e596a
Laura Weidinger
admin_assigned
2024-08-06T11:55:55.907Z
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Laura Weidinger
user
lweidinger
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Robert Stanforth
66b1e42e874d8abe0b6e596c
Leonard Berrada
admin_assigned
2024-08-06T11:56:14.500Z
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Leonard Berrada
user
lberrada
66b1e42e874d8abe0b6e596d
Pushmeet Kohli
66b1e42e874d8abe0b6e596e
Po-Sen Huang
admin_assigned
2024-08-06T11:56:32.948Z
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PH
user
posenhuang
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Borja Balle
admin_assigned
2024-08-06T11:56:39.575Z
668298c155ecd182e4b78afb
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Borja Balle
user
bballe
Operationalizing Contextual Integrity in Privacy-Conscious Assistants
Advanced AI assistants combine frontier LLMs and tool access to autonomously
perform complex tasks on behalf of users. While the helpfulness of such
assistants can increase dramatically with access to user information including
emails and documents, this raises privacy concerns about assistants sharing
inappropriate information with third parties without user supervision. To steer
information-sharing assistants to behave in accordance with privacy
expectations, we propose to operationalize contextual integrity
(CI), a framework that equates privacy with the appropriate flow of information
in a given context. In particular, we design and evaluate a number of
strategies to steer assistants' information-sharing actions to be CI compliant.
Our evaluation is based on a novel form filling benchmark composed of synthetic
data and human annotations, and it reveals that prompting frontier LLMs to
perform CI-based reasoning yields strong results.
Strategies to ensure frontier LLMs perform information-sharing in compliance with privacy expectations through contextual integrity are evaluated with strong results.
LLMs contextual integrity information flow form filling benchmark CI-based reasoning