The Foundation Model Transparency Index
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor, Maslej, Betty Xiong, Daniel Zhang, Percy Liang

TL;DR
This paper introduces the Foundation Model Transparency Index, a comprehensive tool with 100 indicators to evaluate transparency in foundation models, aiming to improve accountability and governance in AI development.
Contribution
It develops a detailed transparency index for foundation models and applies it to major developers, providing a standardized assessment framework.
Findings
No developer discloses downstream impact details
Transparency varies significantly across organizations
The index reveals critical gaps in current transparency practices
Abstract
Foundation models have rapidly permeated society, catalyzing a wave of generative AI applications spanning enterprise and consumer-facing contexts. While the societal impact of foundation models is growing, transparency is on the decline, mirroring the opacity that has plagued past digital technologies (e.g. social media). Reversing this trend is essential: transparency is a vital precondition for public accountability, scientific innovation, and effective governance. To assess the transparency of the foundation model ecosystem and help improve transparency over time, we introduce the Foundation Model Transparency Index. The Foundation Model Transparency Index specifies 100 fine-grained indicators that comprehensively codify transparency for foundation models, spanning the upstream resources used to build a foundation model (e.g data, labor, compute), details about the model itself…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Privacy-Preserving Technologies in Data · Blockchain Technology Applications and Security
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Layer Normalization · Softmax · Dense Connections · Residual Connection · Absolute Position Encodings · Adam · Byte Pair Encoding
