Data Equity: Foundational Concepts for Generative AI
- URL: http://arxiv.org/abs/2311.10741v1
- Date: Fri, 27 Oct 2023 05:19:31 GMT
- Title: Data Equity: Foundational Concepts for Generative AI
- Authors: JoAnn Stonier, Lauren Woodman, Majed Alshammari, Ren\'ee Cummings,
Nighat Dad, Arti Garg, Alberto Giovanni Busetto, Katherine Hsiao, Maui
Hudson, Parminder Jeet Singh, David Kanamugire, Astha Kapoor, Zheng Lei,
Jacqueline Lu, Emna Mizouni, Angela Oduor Lungati, Mar\'ia Paz Canales
Loebel, Arathi Sethumadhavan, Sarah Telford, Supheakmungkol Sarin, Kimmy
Bettinger, Stephanie Teeuwen
- Abstract summary: GenAI promises immense potential to drive digital and social innovation.
GenAI has the potential to democratize access and usage of technologies.
However, left unchecked, it could deepen inequities.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This briefing paper focuses on data equity within foundation models, both in
terms of the impact of Generative AI (genAI) on society and on the further
development of genAI tools. GenAI promises immense potential to drive digital
and social innovation, such as improving efficiency, enhancing creativity and
augmenting existing data. GenAI has the potential to democratize access and
usage of technologies. However, left unchecked, it could deepen inequities.
With the advent of genAI significantly increasing the rate at which AI is
deployed and developed, exploring frameworks for data equity is more urgent
than ever. The goals of the briefing paper are threefold: to establish a shared
vocabulary to facilitate collaboration and dialogue; to scope initial concerns
to establish a framework for inquiry on which stakeholders can focus; and to
shape future development of promising technologies. The paper represents a
first step in exploring and promoting data equity in the context of genAI. The
proposed definitions, framework and recommendations are intended to proactively
shape the development of promising genAI technologies.
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