Evaluating the Social Impact of Generative AI Systems in Systems and
  Society
Generative AI systems across modalities, ranging from text, image, audio, and
video, have broad social impacts, but there exists no official standard for
means of evaluating those impacts and which impacts should be evaluated. We
move toward a standard approach in evaluating a generative AI system for any
modality, in two overarching categories: what is able to be evaluated in a base
system that has no predetermined application and what is able to be evaluated
in society. We describe specific social impact categories and how to approach
and conduct evaluations in the base technical system, then in people and
society. Our framework for a base system defines seven categories of social
impact: bias, stereotypes, and representational harms; cultural values and
sensitive content; disparate performance; privacy and data protection;
financial costs; environmental costs; and data and content moderation labor
costs. Suggested methods for evaluation apply to all modalities and analyses of
the limitations of existing evaluations serve as a starting point for necessary
investment in future evaluations. We offer five overarching categories for what
is able to be evaluated in society, each with their own subcategories:
trustworthiness and autonomy; inequality, marginalization, and violence;
concentration of authority; labor and creativity; and ecosystem and
environment. Each subcategory includes recommendations for mitigating harm. We
are concurrently crafting an evaluation repository for the AI research
community to contribute existing evaluations along the given categories. This
version will be updated following a CRAFT session at ACM FAccT 2023.
6486818b43ba3df95402d2cf
Irene Solaiman
extracted_confirmed
2023-06-12T04:26:55.608Z
62543749b777cd32720675c2
https://cdn-avatars.huggingface.co/v1/production/uploads/1658760912583-62543749b777cd32720675c2.jpeg
Irene Solaiman
user
irenesolaiman
6486818b43ba3df95402d2d0
Zeerak Talat
6486818b43ba3df95402d2d1
William Agnew
6486818b43ba3df95402d2d2
Lama Ahmad
6486818b43ba3df95402d2d3
Dylan Baker
6486818b43ba3df95402d2d4
Su Lin Blodgett
6486818b43ba3df95402d2d5
Hal Daumé III
6486818b43ba3df95402d2d6
Jesse Dodge
admin_assigned
2023-06-12T10:09:47.178Z
6283f38567d336d3e5d5280e
/avatars/d0a54aaec74a90b050e671c191b87a80.svg
Jesse Dodge
user
JesseDodge
6486818b43ba3df95402d2d7
Ellie Evans
admin_assigned
2023-06-12T10:10:45.057Z
626c0912974d6a67df700db6
/avatars/057a19f682e6a8d0411bc6efa25f2588.svg
Ellie Evans
user
ellieevans
6486818b43ba3df95402d2d8
Sara Hooker
admin_assigned
2023-06-12T10:11:04.380Z
63434eb76f59b79da07dbddf
https://cdn-avatars.huggingface.co/v1/production/uploads/63434eb76f59b79da07dbddf/BEwmVjqPNYlqmutXG0G6e.jpeg
Sara Hooker
user
sarahooker
6486818b43ba3df95402d2d9
Yacine Jernite
admin_assigned
2023-06-12T10:07:51.393Z
5ee3a7cd2a3eae3cbdad1305
https://cdn-avatars.huggingface.co/v1/production/uploads/1594144055859-5ee3a7cd2a3eae3cbdad1305.jpeg
Yacine Jernite
user
yjernite
6486818b43ba3df95402d2da
Alexandra Sasha Luccioni
admin_assigned
2023-06-12T10:08:05.485Z
60edd0133e2c73a9a21455f5
https://cdn-avatars.huggingface.co/v1/production/uploads/60edd0133e2c73a9a21455f5/yK1G-Fv-YjYb7v_chkz3p.jpeg
Sasha Luccioni
user
sasha
6486818b43ba3df95402d2db
Alberto Lusoli
6486818b43ba3df95402d2dc
Margaret Mitchell
admin_assigned
2023-06-12T10:08:18.780Z
60c757ea5f9a76ab3f844f12
https://cdn-avatars.huggingface.co/v1/production/uploads/1626214544196-60c757ea5f9a76ab3f844f12.png
Margaret Mitchell
user
meg
6486818b43ba3df95402d2dd
Jessica Newman
6486818b43ba3df95402d2de
Marie-Therese Png
6486818b43ba3df95402d2df
Andrew Strait
6486818b43ba3df95402d2e0
Apostol Vassilev
Evaluating the Social Impact of Generative AI Systems in Systems and
  Society
Generative AI systems across modalities, ranging from text, image, audio, and
video, have broad social impacts, but there exists no official standard for
means of evaluating those impacts and which impacts should be evaluated. We
move toward a standard approach in evaluating a generative AI system for any
modality, in two overarching categories: what is able to be evaluated in a base
system that has no predetermined application and what is able to be evaluated
in society. We describe specific social impact categories and how to approach
and conduct evaluations in the base technical system, then in people and
society. Our framework for a base system defines seven categories of social
impact: bias, stereotypes, and representational harms; cultural values and
sensitive content; disparate performance; privacy and data protection;
financial costs; environmental costs; and data and content moderation labor
costs. Suggested methods for evaluation apply to all modalities and analyses of
the limitations of existing evaluations serve as a starting point for necessary
investment in future evaluations. We offer five overarching categories for what
is able to be evaluated in society, each with their own subcategories:
trustworthiness and autonomy; inequality, marginalization, and violence;
concentration of authority; labor and creativity; and ecosystem and
environment. Each subcategory includes recommendations for mitigating harm. We
are concurrently crafting an evaluation repository for the AI research
community to contribute existing evaluations along the given categories. This
version will be updated following a CRAFT session at ACM FAccT 2023.