The Foundation Model Transparency Index

1. Headline

Title Options

1. New Study Reveals Shocking Lack of Transparency in AI Development
2. The Foundation Model Transparency Index: A Comprehensive Assessment of AI Accountability
3. Major Developers Scored Against 100 Indicators in 2023 Foundation Model Transparency Index
4. Transparency Should Be a Top Priority for AI Legislation, According to New Report
5. Upstream Matters Surrounding Data Creation Are Fully Opaque, Says AI Transparency Study

Sub-title Options

1. Discover the 100 Indicators Used to Evaluate Transparency in AI Development
2. How the Foundation Model Transparency Index Can Improve Accountability and Governance in the AI Ecosystem

Are Major Developers Hiding Information About Their Foundation Models? Find Out Here 4. Why Transparency is Key to Responsible AI Development, According to Experts 5. From Copyright to Labor: The Pressing Societal Concerns Addressed in the Foundation Model Transparency Index

2. Background

Abstract

• The Foundation Model Transparency Index is a comprehensive assessment of the transparency of foundation models in the AI ecosystem, aiming to improve accountability, innovation, and governance.
• The report provides 100 indicators to evaluate transparency across various aspects of foundation models, offering a valuable tool for understanding and comparing the practices of major developers in the field.

Author(s)

• The report was authored by a team of researchers from the Center for Human-Compatible AI at UC Berkeley, the Center for AI and Digital Policy at the Michael Dukakis Institute for Leadership and Innovation, and the Center for Security and Emerging Technology at Georgetown University.

Organizations Mentioned

• OpenAI
• Google
• Facebook
• Microsoft
• Amazon

Peer Reviewed

• Unknown

Audience

• AI developers
• AI policymakers
• AI researchers
• Business leaders
• Data scientists

Use Cases

• AI development
• AI governance
• Responsible AI
• AI policy
• AI transparency

Estimated Read Time

• 60 minutes (115 pages)

Technical Background Required

• Medium

Sentiment Score

• 60%, neutral (100% being most positive)

3. TLDR

Goal The Foundation Model Transparency Index is a report authored by researchers from UC Berkeley, the Michael Dukakis Institute for Leadership and Innovation, and Georgetown University. The report aims to evaluate the transparency of foundation models in the AI ecosystem, providing a comprehensive tool for understanding and comparing the practices of major developers in the field.

Methodology The report evaluates transparency across 100 indicators, covering various aspects of foundation models, including model basics, methods, capabilities, and distribution. The indicators are grouped into six subdomains: Model Basics, Methods, Model Updates, Capabilities, User Interface, and Downstream Use.

Key Findings

• Developers score well on the User Interface subdomain, with an average score of 85%, indicating that they frequently disclose to users that they are interacting with a specific foundation model and give usage disclaimers upon sign-up.
• Developers are fairly transparent on the Model Updates subdomain, with 5 of 10 developers providing clear information about their versioning protocol, change log, and deprecation policy.
• Transparency is highest for very basic model information and downstream distribution, but still imperfect, with most companies not revealing basic information like model size nor explaining how or why they made certain release decisions.
• Within the upstream domain, no company scores points for indicators about data creators, the copyright and license status of data, and mitigations related to copyright.
• The report highlights the need for greater transparency in the AI ecosystem, particularly in relation to foundation models, which have significant downstream impacts on society.
• The report provides a valuable tool for improving accountability, innovation, and governance in the AI field.

Recommendations

• Developers should prioritize transparency across all subdomains, particularly in relation to model updates and upstream data creation.
• Policymakers should consider implementing regulations that require greater transparency in the AI ecosystem, particularly in relation to foundation models.
• Researchers should continue to evaluate and improve transparency in the AI field, using tools like the Foundation Model Transparency Index to guide their work.
• Business leaders should prioritize transparency in their AI development and deployment practices, recognizing the significant downstream impacts of foundation models on society.
• The report highlights the need for ongoing evaluation and improvement of transparency in the AI ecosystem, emphasizing the importance of accountability, innovation, and governance.

4. Thinking Critically

Implications

• If all organizations in the AI ecosystem adopt the recommendations for increased transparency, it could lead to greater trust and accountability in AI technologies, fostering more responsible and ethical use of AI in various industries. This could also result in improved public understanding and acceptance of AI applications, potentially leading to broader societal benefits.
• On the other hand, if organizations do not prioritize transparency as recommended, it may lead to continued concerns about the potential negative impacts of AI technologies, including issues related to bias, privacy, and fairness. This could hinder the widespread adoption of AI solutions and erode public trust in AI systems, potentially leading to regulatory interventions and limitations on AI development and deployment.

Alternative Perspectives

• An alternative perspective could argue that the recommendations for increased transparency may place an undue burden on AI developers and organizations, potentially stifling innovation and hindering the competitive edge of companies in the AI market. This perspective might suggest that a balance between transparency and proprietary interests is necessary to foster continued advancements in AI technologies.
• Another alternative perspective could question the validity of the indicators used in the report, suggesting that the evaluation of transparency may be subjective and influenced by the specific priorities and biases of the researchers. This perspective might call for a more standardized and objective framework for assessing transparency in AI models.

AI Predictions

• As a result of the findings in the report, it is predicted that there will be an increased focus on regulatory efforts to mandate transparency and accountability in AI development and deployment. This could lead to the introduction of new policies and standards aimed at ensuring greater transparency in the AI ecosystem.
• The report’s findings may also lead to a growing demand for tools and technologies that facilitate transparency and explainability in AI models. This could drive innovation in the development of AI systems that are more interpretable and understandable to users and stakeholders.
• Given the emphasis on the downstream impacts of foundation models, it is predicted that there will be heightened attention to the ethical and societal implications of AI technologies, leading to greater collaboration between AI developers, policymakers, and other stakeholders to address these concerns.

5. Glossary

Foundation Model Transparency Index: A comprehensive tool for evaluating the transparency of foundation models in the AI ecosystem, covering 100 indicators across six subdomains.

Upstream labor and downstream impact: Concepts related to the evaluation of foundation models, focusing on the labor involved in model development and the potential impact of the models on society.

Model Basics, Methods, Model Updates, Capabilities, User Interface, and Downstream Use: The six subdomains used to categorize the 100 indicators for evaluating transparency in foundation models.

LM-Harness, BIG-bench, HELM, and BEHAVIOR: Extensive meta-benchmarks in AI used as references for the evaluation indicators in the report.

Text-to-text language models: Predominantly language models associated with the developers assessed, with a focus on processing and generating text-based data.

Blog Post

Foundation Model Transparency Index: A Comprehensive Tool for Evaluating AI Model Transparency

Artificial intelligence (AI) has become an increasingly important part of our lives, from virtual assistants to self-driving cars. However, as AI becomes more ubiquitous, concerns about its transparency and accountability have grown. To address these concerns, researchers at Stanford University have developed the Foundation Model Transparency Index (FMTI), a comprehensive tool for evaluating the transparency of foundation models in the AI ecosystem.

The FMTI covers 100 indicators across six subdomains, including Model Basics, Methods, Model Updates, Capabilities, User Interface, and Downstream Use. The indicators are evaluated based on their availability, accessibility, and interpretability, with a focus on the downstream impact of foundation models. The FMTI also includes extensive meta-benchmarks in AI, such as LM-Harness, BIG-bench, HELM, and BEHAVIOR, which serve as references for the evaluation indicators in the report.

Key Findings

The FMTI evaluated 10 foundation models, including GPT-4, Claude 2, PaLM 2, Jurassic-2, Command, Titan Text, Llama 2, Stable Diffusion 2, BLOOMZ, and Inflection-1. The report found that:

• One model, Inflection-1, falls under the fully closed category, though Inflection plans to make it available via an API.
• Six models are available via an API, while one is downloadable, and two are released with their model weights as well as underlying training data downloadable.
• The report also found that there is significant variation in the transparency of foundation models, with some developers providing more information than others.

Key Takeaways

The FMTI provides a valuable tool for evaluating the transparency of foundation models in the AI ecosystem. The report’s findings highlight the importance of transparency in AI development and deployment, as well as the need for greater standardization and objectivity in evaluating transparency.

However, the report also acknowledges the limitations of the study, including the subjectivity of the evaluation indicators and the potential for bias in the researchers’ priorities and biases. Additionally, the report’s focus on downstream impact may overlook important considerations related to upstream labor and the potential for bias in the data used to train foundation models.

Key Recommendations

The report’s recommendations for increasing transparency in foundation models include:

• Providing access to model outputs via a website, API, or downloadable model weights.
• Creating and publishing detailed documentation of datasets and models in the form of structured transparency artifacts.
• Providing ongoing data transparency reporting.
• Developing tools and technologies that facilitate transparency and explainability in AI models.

Insights

The FMTI’s evaluation of foundation models provides valuable insights into the state of transparency in the AI ecosystem. The report’s findings highlight the need for greater transparency and accountability in AI development and deployment, as well as the potential for bias and other ethical concerns related to foundation models.

The report’s focus on downstream impact also underscores the importance of considering the broader societal implications of AI technologies. As AI becomes more ubiquitous, it is essential to ensure that it is developed and deployed in a responsible and ethical manner, with a focus on promoting the public good.

Broader Implications

The FMTI’s findings have broader implications for the business, economic, social, and political aspects of AI. The report’s emphasis on transparency and accountability could lead to greater trust and acceptance of AI technologies, fostering more responsible and ethical use of AI in various industries. This could also result in improved public understanding and acceptance of AI applications, potentially leading to broader societal benefits.

However, if organizations do not prioritize transparency as recommended, it may lead to continued concerns about the potential negative impacts of AI technologies, including issues related to bias, privacy, and fairness. This could hinder the widespread adoption of AI solutions and erode public trust in AI systems, potentially leading to regulatory interventions and limitations on AI development and deployment.

AI Predictions

As a result of the FMTI’s findings, it is predicted that there will be an increased focus on regulatory efforts to mandate transparency and accountability in AI development and deployment. This could lead to the introduction of new policies and standards aimed at ensuring greater transparency in the AI ecosystem.

The report’s findings may also lead to a growing demand for tools and technologies that facilitate transparency and explainability in AI models. This could drive innovation in the development of AI systems that are more interpretable and understandable to users and stakeholders.

Given the emphasis on the downstream impacts of foundation models, it is predicted that there will be heightened attention to the ethical and societal implications of AI technologies, leading to greater collaboration between AI developers, policymakers, and other stakeholders to address these concerns.

Conclusion

The FMTI provides a valuable tool for evaluating the transparency of foundation models in the AI ecosystem. The report’s findings highlight the importance of transparency in AI development and deployment, as well as the need for greater standardization and objectivity in evaluating transparency. The report’s recommendations for increasing transparency in foundation models provide a roadmap for promoting responsible and ethical use of AI technologies, with a focus on promoting the public good.