REVIEW 10 cited by
The Foundation Model Transparency Index
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
The Foundation Model Transparency Index
read the original 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 (e.g. size, capabilities, risks), and the downstream use (e.g. distribution channels, usage policies, affected geographies). We score 10 major foundation model developers (e.g. OpenAI, Google, Meta) against the 100 indicators to assess their transparency. To facilitate and standardize assessment, we score developers in relation to their practices for their flagship foundation model (e.g. GPT-4 for OpenAI, PaLM 2 for Google, Llama 2 for Meta). We present 10 top-level findings about the foundation model ecosystem: for example, no developer currently discloses significant information about the downstream impact of its flagship model, such as the number of users, affected market sectors, or how users can seek redress for harm. Overall, the Foundation Model Transparency Index establishes the level of transparency today to drive progress on foundation model governance via industry standards and regulatory intervention.
Forward citations
Cited by 10 Pith papers
-
Macro-Prudential AI Governance: A Two-Layer Early Warning and Response System for Frontier AI
A Basel-III-style two-layer system—coordinated finder-coordinator-defender reporting plus ECAR, CRTH, and ARS buffers—can detect and dampen correlated risk build-up across frontier AI labs’ internal deployments.
-
M\"OVE: A Holistic LLM Benchmark for the German Public Sector
MÖVE presents a new German-language benchmark evaluating 39 LLMs on performance and governance criteria using ten public-administration datasets.
-
A governance horizon for ethical-use constraints in open-weight AI models
Ethical constraint evidence on open-weight AI models decays with a half-life of 1.31 derivation steps on Hugging Face, creating a governance horizon at seven generations where 80% of models lack traceable information.
-
The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems
The 2025 AI Agent Index catalogs technical and safety details for 30 deployed AI agents and finds low developer transparency on safety, evaluations, and societal impacts.
-
A Study of LLMs' Preferences for Libraries and Programming Languages
Empirical study of eight LLMs finds overuse of popular libraries like NumPy in up to 45% of unnecessary cases and strong default preference for Python even when suboptimal.
-
Laissez-Faire Harms: Algorithmic Biases in Generative Language Models
Generative LMs in laissez-faire open-ended prompting settings disproportionately generate subordinated portrayals of minoritized race, gender, and sexual orientation identities at rates hundreds to thousands of times ...
-
TRUST: A Framework for Decentralized AI Service v.0.1
TRUST is a decentralized AI auditing framework that decomposes reasoning into HDAGs, maps agent interactions via the DAAN protocol to CIGs, and uses stake-weighted multi-tier consensus to achieve 72.4% accuracy while ...
-
Mind Your Tone: Does Tone Alter LLM Performance?
Tonal variations in prompts cause systematic but model-dependent accuracy changes in LLMs on objective multiple-choice questions.
-
Human-aligned AI Model Cards with Weighted Hierarchy Architecture
Introduces CRAI-MCF, an eight-module framework distilling 217 parameters from 240 projects into a quantitative sufficiency criterion for cross-model LLM comparison grounded in Value Sensitive Design.
-
Taxing Artificial Intelligence
Taxation of AI activities can correct externalities, redistribute costs and gains, and support regulation, though instruments like corporate taxes, consumption taxes, and excises vary in feasibility, measurement chall...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.