In the rapidly evolving landscape of artificial intelligence, Open Weights have emerged as a buzzword indicating incremental progress in AI transparency. By sharing the final parameters of a trained model, developers offer some insight into how a neural network operates. However, these weights reveal only a fraction of the information required for full accountability. While Open Weights represent a milestone in opening up AI systems, they still stop short of delivering the level of transparency many researchers and regulators deem essential.

What are Open Weights?

Open Weights refer to the final weights and biases of a trained neural network. These values, once locked in, determine how the model interprets input data and generates outputs. When AI developers share these parameters under an OSI Approved License, they empower others to fine-tune, adapt, or deploy the model for their own projects.

However, Open Weights differ significantly from Open Source AI because they do not include:

1. Training code – The scripts or frameworks used to create and curate the training dataset.
2. Training dataset – The full dataset used for training, when legally possible. As an alternative, when distribution of the training dataset is not legally possible,
   1. Comprehensive data transparency – Full details about dataset composition, such as source domains, cleaning methods, or balancing techniques.

By withholding these critical elements, developers only provide a glimpse into the final state of the model, making it difficult for others to replicate, audit, or deeply understand the training process.

Is Open Weights a new concept?

Far from it. Over the past decade, AI practitioners have experimented with different ways of sharing or withholding information, often balancing trade secrets with mounting calls for AI transparency. The renewed interest in Open Weights arose in response to regulatory scrutiny and growing awareness that completely opaque systems can embed biases and discriminatory behaviors.

In 2023, Heather Meeker, a recognized expert in Open Source licensing, published an Open Weights Definition that formalized many of these conversations. Her work clarifies permissible usage and distribution of final model parameters while highlighting a gap: the full process behind model creation still remains undisclosed. As discussions on Open Source AI intensify, so does the debate around whether Open Weights alone can deliver the transparency needed for ethical and responsible AI.

The limitations of Open Weights

While Open Weights stand out as more transparent than purely proprietary AI, they still lack several key elements of Open Source AI.

1. Lack of reproducibility

Reproducibility is critical in scientific and technological progress. Without training code or intermediate checkpoints, researchers and auditors cannot replicate the model’s development process. This gap hinders efforts to identify when and where biases might have been introduced, making it nearly impossible to rectify errors or vulnerabilities.

2. Data opacity

The phrase “garbage in, garbage out” applies strongly to AI. If the training data is not representative or ethically sourced, the model’s outputs can exhibit harmful biases. However, Open Weights often do not clarify how the dataset was constructed or cleaned. This oversight leaves a significant blind spot, preventing anyone outside the original development team from fully assessing the dataset’s quality or diversity.

3. Regulatory hurdles

Governments worldwide are formulating policies that mandate higher standards of transparency in AI, especially for systems deployed in sensitive areas such as finance, healthcare, and public administration. Disclosing only the final weights may not meet these emerging regulations, as the lack of training code or dataset details could violate requirements for fairness, privacy, or explainability.

4. Limited community collaboration

One of the core strengths of Open Source AI lies in the collaborative potential it unlocks. When the entire pipeline—training scripts, dataset composition, and intermediate checkpoints—is openly available, a global community can work together to improve the model, fix bugs, or address ethical concerns. By contrast, Open Weights significantly reduce these possibilities, limiting meaningful contributions to superficial fine-tuning rather than in-depth improvements.

Open Weights vs. Open Source AI

Open Source AI’s four freedoms

Following the same idea behind open source software, an Open Source AI is made available under terms that grant users the following freedoms:

1. Use – The freedom to use the system for any purpose without seeking additional permission.
2. Study – The freedom to study how the system works and understand how its results are generated.
3. Modify – The freedom to modify the system for any purpose, including changing its outputs.
4. Share – The freedom to share the system with others, with or without modifications, for any purpose.

A fundamental precondition to exercise these freedoms is having access to the preferred form needed to make modifications, and the practical means to use it. Open Weights alone fall short of this because they do not provide the underlying training process, code, or comprehensive data details required for full-fledged use, study, modification, and sharing.

To better understand why Open Weights and Open Source AI differ so drastically, consider the following comparison:

Feature	Open Weights	Open Source AI
Weights & Biases	Released	Released
Training Code	Not Shared	Fully Shared
Intermediate Checkpoints	Withheld	Nice to have
Training dataset	Not Shared/Not disclosed	Released*
Training Data Composition	Partially/Not Disclosed	Fully Disclosed

Clearly, Open Weights mark a notable advancement over fully proprietary solutions by offering the final model parameters. However, Open Source AI goes further by unlocking the entire development process. This holistic openness enables complete reproducibility, thorough bias audits, and robust community-driven improvements.

* When legally allowed. See Open Source AI Definition FAQ.

Why transparency in AI matters

Ethical AI development

A model’s fairness depends heavily on data quality and balanced training procedures. Open source AI allows reviewers to spot and address potential biases early, while Open Weights alone can’t provide enough context to guarantee ethical performance.

Regulatory compliance

Policymakers need concrete proof that AI models comply with laws on privacy, discrimination, and consumer protection. With Open Weights, regulators see only the end result, not the steps taken to reach it. Full openness eases the burden of proving a model’s compliance across various jurisdictions.

Innovation and collaboration

When experts worldwide can inspect training code, dataset details, and, ideally, intermediate checkpoints, they can collectively refine algorithms, fix bugs, and broaden the model’s applicability. This communal effort drives forward innovation in a way that Open Weights alone cannot match.

Trust and public perception

In an era of data breaches and algorithmic controversies, public trust in AI remains fragile. Models that offer complete transparency—which is the hallmark of Open Source AI—are more likely to gain acceptance from stakeholders who worry about issues such as hidden biases or unaccountable decision-making.

The role of Open Weights: a lesser evil?

Many see Open Weights as a compromise—a lesser evil than completely proprietary AI. By at least making the final parameters accessible, developers provide some degree of insight into the model’s decision logic. This can be enough for certain low-stakes applications where minimal accountability suffices.

However, for industries like healthcare, autonomous vehicles, or financial underwriting—where AI decisions carry significant consequences—the partial transparency of Open Weights is insufficient. Full accountability demands understanding not just the final model, but also how it was built, the data it relied on, and the points at which it might have diverged from ethical best practices.

The bottom line

Open Weights might seem revolutionary at first glance, but they’re merely a starting point. While they do move the needle closer to transparency than strictly closed, proprietary models, they lack the detailed insights found in Open Source AI. For AI to be both accountable and scalable, every part of the pipeline—from the initial dataset to the final set of parameters—needs to be open to scrutiny, validation, and collective improvement.

If you care about AI systems that are trustworthy, fair, and compliant with upcoming regulations, look beyond Open Weights. Learn more about Open Source AI, where full reproducibility and transparency foster a healthier, more innovative ecosystem. By championing this evolution, we can move closer to AI solutions that benefit everyone, not just a select few.

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