WorkDifferentWithAI.com Academic Paper Alert!
Written by Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, Percy Liang
Category: “AI Strategy & Governance”
Article Section: AI Strategy and Governance; AI Governance Frameworks
Publication Date: 2023-10-19
SEO Description: Exploring the transparency of AI Foundation Models, assessing developers like OpenAI, Google & Meta, for effective governance.
AI-Generated Paper Summary
GPT-4 API
The Foundation Model Transparency Index is a novel initiative that has been proposed to monitor and enhance the transparency of foundation models in AI. These models have made a significant influence on society and have been adopted by a wide range of generative AI applications across various sectors. However, the transparency of these models is dwindling due to the lack of clarity which in turn hinders public accountability, scientific innovation, and effective governance. This transparency index introduces 100 fine-tuned indicators to measure transparency across three segments: the resources used to create the model, details about the model itself, and the model’s downstream usage. The transparency of major foundation model developers, such as OpenAI, Google, Meta, is evaluated against these indicators. The ultimate objective of the index is to expedite the progress on foundation model governance via industry standards and regulatory intervention.
Claude.ai Full PDF Analysis
Based on my review, here are the key points about the novelty and potential commercial applications of this paper introducing the Foundation Model Transparency Index:
Novelty
- Proposes a new composite index specifically focused on assessing the transparency of foundation model developers. Indexes are uncommon in AI, so this represents a novel methodology.
- Provides an extensive taxonomy of transparency with 100 concrete indicators spanning upstream resources, the model itself, and downstream use/impact. This comprehensive set of indicators is novel.
- Directly scores 10 major foundation model developers on the index indicators using a standardized protocol. Assessing companies in this way is novel in the context of foundation models.
- Presents numerous empirical findings about the current lack of transparency, with transparency scores for developers overall and broken down across domains and dimensions. This data-driven characterization of the status quo is novel.
- Makes the indicators, developer scores, and all materials public to enable future research. This level of transparency in conducting the assessment is novel.
Commercial Applications
- The taxonomy of 100 indicators could be valuable for companies aiming to implement transparency practices or frame transparency requirements internally.
- The scoring provides a benchmark that companies can use to compare their transparency to competitors, identifying gaps and opportunities for improvement.
- The findings can guide companies on where transparency is most lacking across the ecosystem but feasible, clarifying promising areas to lead on transparency.
- The public data enables third parties to conduct proprietary analyses or derivative evaluations pertinent to their specific business needs.
- The indicators and scoring methodology could be adapted to assess transparency of AI systems beyond foundation models, for commercial AI providers.
- Subsequent iterations of the index could become an industry benchmark to certify companies as transparent, which could provide commercial value.
In summary, the paper makes both methodological and empirical contributions that could inform practices, strategies, and policies related to transparency in commercial AI development and deployment.
Keywords
Foundation Model Transparency Index, generative AI applications, transparency, foundation model governance, industry standards
Author’s 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.