[Research Paper Summary]The Foundation Model Transparency Index
Original Paper: https://arxiv.org/abs/2310.12941
By: Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, Percy Liang
Abstract:
Foundation models have swiftly infiltrated society, initiating a surge of generative AI applications in both enterprise and consumer domains.
The societal influence of foundation models is increasing, although openness is diminishing, reflecting the opacity that has characterized previous digital innovations (e.g., social media).
Altering this trend is imperative: transparency is a crucial prerequisite for public accountability, scientific advancement, and efficient governance.
We present the Foundation Model Transparency Index to evaluate and enhance the transparency of the foundation model ecosystem over time.
The Foundation Model Transparency Index delineates 100 detailed indicators that thoroughly encapsulate transparency for foundation models, encompassing the upstream resources utilized in their construction (e.g., data, labor, compute), specifics regarding the model itself (e.g., size, capabilities, risks), and the downstream applications (e.g., distribution channels, usage policies, impacted geographies).
We evaluate 10 prominent foundation model developers (e.g., OpenAI, Google, Meta) using 100 metrics to measure their transparency.
To streamline and standardize evaluation, we assess developers based on their methodologies for their primary foundation model (e.g., GPT-4 for OpenAI, PaLM 2 for Google, Llama 2 for Meta).
We propose ten principal conclusions regarding the foundation model ecosystem: For instance, no developer presently reveals substantial information regarding the downstream effects of its flagship model, including user numbers, impacted market sectors, or the avenues available for users to seek recourse for harm.
The Foundation Model Transparency Index assesses the current level of transparency to promote advancements in foundation model governance through industry standards and legislative measures.
Summary Notes
Introduction
In recent years, foundational models such as OpenAI’s GPT-4 and Meta’s LLaMA have transformed artificial intelligence, facilitating a proliferation of generative AI applications across diverse sectors.
Despite their swift assimilation into society, transparency in the development and implementation of these models has diminished, reflecting the obscurity observed in earlier digital technologies such as social media.
In response to this pressing concern, researchers from Stanford, MIT, and Princeton have developed the Foundation Model openness Index (FMTI), a comprehensive methodology aimed at evaluating and enhancing openness in foundation models.
This blog article examines the techniques, conclusions, and consequences of the FMTI, and explores its potential to advance transparency and accountability in AI.
Key Methodologies
The FMTI assesses transparency in three key areas: upstream, model, and downstream. Each domain is subdivided into subdomains, with 100 detailed metrics that jointly assess transparency.
These indicators encompass multiple facets, including data sources, labor practices, computational resources, model capabilities, dangers, and downstream effects.
The study evaluated ten prominent foundation model developers, such as OpenAI, Google, Meta, and Stability AI, by evaluating them on 100 variables to establish a detailed transparency profile for each.
The scoring process entailed a systematic search protocol, stringent independent assessment by researchers, and input from the developers to guarantee precision and replicability.
Main Findings
1. Comprehensive Transparency Ratings: The maximum overall score achieved was 54 out of 100, with Meta at the forefront, although the mean score across all developers was 37. This signifies considerable potential for enhancement in transparency universally.
2. Scores at the Domain Level: Transparency was minimal in the upstream sector, especially regarding data, data labor, and computation. Developers achieved an average score of merely 22.5% in upstream openness, in contrast to 42.7% in model transparency and 44.9% in downstream transparency.
3. High-Scoring Subdomains: Developers exhibited the highest level of transparency regarding the fundamental aspects of the model, its capabilities, and the channels for downstream distribution. Nonetheless, even in these domains, significant deficiencies were evident, including insufficient transparency regarding model size and deprecation regulations.
4. Subdomains with Low Scores: Transparency was least effective regarding labor practices, computational consumption, and downstream effects. No developer supplied detailed information regarding downstream applications, impacted individuals, or remedial measures.
5. Open versus Closed Models: Developers of open models (e.g., Meta, Hugging Face, Stability AI) exhibited greater transparency compared to their closed equivalents (e.g., OpenAI, Google). Open developers had superior scores on upstream and model metrics, however exhibited comparable transparency levels in downstream metrics.
Implications and Applications
The FMTI’s findings highlight the pressing necessity for enhanced transparency within the foundation model ecosystem. Below are few possible applications and ramifications:
1. Policy and Regulation: Policymakers can utilize the FMTI to guide AI policies, guaranteeing that transparency mandates are grounded in proof and focused on the most obscure sectors. For instance, particular transparency requirements regarding data labor practices and their downstream effects should be emphasized.
2. Sector Norms: The FMTI can facilitate the formulation of industry standards for transparency, aiding in the establishment of best practices and benchmarks that all developers should aspire to achieve. This can cultivate a culture of transparency and responsibility in AI development.
3. Public Trust and Accountability: Augmented transparency can bolster public confidence in AI systems by furnishing users and stakeholders with the requisite knowledge to comprehend and assess the ramifications of these technologies. This is essential for guaranteeing ethical AI implementation and alleviating possible risks.
4. Investigation and Advancement: By pinpointing transparency deficiencies, the FMTI can catalyze additional research on efficacious transparency measures and instruments, including enhanced documentation processes, independent audits, and accessible transparency reporting systems.
Conclusion
The Foundation Model Transparency Index provides a thorough and practical methodology for assessing and enhancing transparency in artificial intelligence.
Although the existing level of transparency is suboptimal, the FMTI delineates a definitive course of action.
By implementing the ideas and findings from this study, developers, policymakers, and stakeholders may collaboratively establish a more transparent, accountable, and trustworthy AI ecosystem.
Individuals seeking to explore the intricacies of the FMTI, encompassing comprehensive ratings and rationales for each developer, can access the complete report and supplementary materials on the project’s [GitHub repository] (https://www.github.com/stanford-crfm/fmti).
Feel free to check out more blogs, research paper summaries and resources on AI by visiting our website.
