New ‘Open Weight Definition’ seeks to clarify the real difference between open- and closed-source AI models

The Open Source Alliance has introduced a draft Open Weight Definition (OWD) to standardize and clarify the relationship between open and closed-source AI models.

Core initiative: The Open Weight Definition aims to establish clear guidelines for AI model accessibility while protecting essential freedoms of software use and sharing.

• The definition allows users to download and deploy AI technologies without charge or permission requirements
• This framework maintains two of the four essential freedoms of free software: the ability to use and share, though not necessarily to study or modify models
• The approach is designed to lower barriers to entry for vendors who cannot yet meet full open source requirements

Key distinctions: The OWD creates important clarifications between truly open source AI and models that share limited or no data.

• Previously, terms like “open source” and “open weight” were often used interchangeably despite significant differences
• The definition helps distinguish between fully open models and those distributed without reproduction essentials
• Clear labeling and responsible use guidelines ensure users understand model limitations and available freedoms

Expert perspectives: Industry leaders emphasize the importance of a pragmatic, disaggregated approach to AI openness.

• Amanda Brock, CEO at OpenUK, supports defining levels of openness across different AI components rather than attempting to create a single comprehensive definition
• The approach acknowledges that legal and privacy constraints may affect data accessibility without compromising the broader goal of openness
• Sam Johnston, convenor of the Open Source Alliance, highlights how open weight models serve as essential tools for innovation

Practical implications: The framework creates a balanced approach while acknowledging certain trade-offs.

• Users maintain limited opportunities to study and modify models through observation and fine-tuning
• Challenges remain in addressing ethical issues related to fairness and bias in training data
• The inability to fully modify or retrain models is considered an acceptable compromise for many applications

Looking ahead: The introduction of the Open Weight Definition represents a significant step toward creating a more collaborative AI ecosystem where proprietary and open-source solutions can coexist effectively, though questions remain about how this framework will evolve as AI technology continues to advance and new challenges emerge.