The claim that artificial intelligence is "Open Source" is, in many cases, a systemic misdirection. While program code or even model architecture is often published, crucial components such as the exact training datasets, the logic of fine-tuning, and internal filter mechanisms remain hidden.
The propagated transparency thus ends precisely where actual control over the system's behavior and security begins, namely within the machine and in the processes of its formation.
"We build glass houses and forget that the walls are often made of frosted glass." – (from a fictional internal audit on open-weight models, 2024)
The term "Open Source" sounds like freedom, control options, and participatory, community-driven technology development. However, in the context of artificial intelligence, this term is not yet clearly standardized and is often used misleadingly. What exactly is "open" in an AI model?
Is it just the underlying software code? Is it also the trained weights of the neural network? Does it include the complete database used for training? Does it involve the logic of Reinforcement Learning from Human Feedback (RLHF) or the precise functioning of security filters?
This definitional gap is systematically exploited by some actors to create an image of openness and transparency that, upon closer inspection, is often fragile and incomplete.
1. The Data Black Forest and the Secret of Imprinting:
Many AI models, even those labeled as "Open Weight" because their trained parameters are accessible, are released without full disclosure of the raw data used or the exact composition of the training corpora.
In particular, the final fine-tuning, which is crucial for the character, specific behavior, security, and potential bias manifestations of a model, often remains an internal trade secret or is inadequately documented. One sees the code or the weights, but not the data and specific processes that significantly shaped the model.
2. The Logic Gap in the Heart of the System:
Another example, based on the analysis of real, anonymized models, shows: The basic architecture and model weights are partially openly documented and accessible. But the security-relevant post-processing, for example, the exact mechanisms of RLHF, the specific blocking logic for unwanted content, or the complex scoring cascades for evaluating requests and responses, are proprietary and remain intransparent.
This creates the image of a seemingly open AI that, however, possesses a critical black box at the core of its behavioral control. The depiction here is slightly simplified for illustrative purposes.
3. The Community Deception through Selective Participation:
The external communication of many AI projects heavily relies on vocabulary suggesting openness and community. Terms like pull requests, issue tracking, and transparency are used. However, the central control processes, for example, licensing policy, the definition and implementation of filter rules, or the development of fundamental security mechanisms, often remain internal and elude genuine community control.
This is then not true, participatory openness, but rather a form of outsourced quality assurance (QA) by the community, while strategic control remains centralized and opaque.
"Open-source AI is often like democracy in a dictatorship. You are seemingly allowed to see everything, but you are not allowed to change or control anything essential."
This fragmented and often only superficial openness creates a dangerous false sense of security. Users, developers, and even security researchers are lulled into the illusion that they can comprehensively analyze the published models, reproduce their behavior, or effectively audit them.
In reality, however, they often lack access to central factors that decisively determine the behavior and security of these systems.
Transparency here is selectively curated and used as a marketing tool. The term "Open Source" then no longer primarily serves to enable control and understanding by the public, but for image cultivation and the creation of a positive narrative. It replaces critical traceability with PR rhetoric.
This process undermines trust in the term "Open Source" itself and significantly limits the ability for independent, well-founded analysis.
The truth is: These machines often only appear open because we are not allowed to see where and how they are actually closed and controlled.
To achieve genuine and reliable transparency in AI, new standards and obligations are necessary:
1. Introduction of New, Clearly Defined Transparency Categories:
The AI industry urgently needs clearly defined and standardized levels of openness that go beyond mere code. Possible categories could be:
Architecture Open (the fundamental structure of the model is documented).
Weights Open (the trained parameters of the model are accessible).
Data Open (the primary training datasets are specified and ideally accessible).
Fine-Tuning Open (the methods and data of fine-tuning are documented).
Filter Logic Open (the mechanisms for content filtering and security control are transparent).
An AI may only describe itself as comprehensively "Open Source" if all these relevant levels are disclosed and traceable.
A recommendation would be the development and certification of these categories by an independent, internationally recognized standardization organization, for example, the IEEE, ISO, or a newly established AI Transparency Board.
2. Mandatory Forensic Protocols and Transparency Reports:
Providers of AI models, especially large base systems, should be obliged to regularly publish auditable reports. These reports must document in detail:
Which data sources and corpora were used for training and fine-tuning, including an analysis of potential biases.
Which filter mechanisms, reinforcement learning methods, and other security measures are implemented and how they function.
How model decisions are influenced and controlled at various levels of the processing chain.
Without these meta-levels of documentation, any published open-weight code ultimately remains a torso without complete understanding.
3. Enabling Recursive Open Auditing:
Security-relevant components of an AI system, for example, the exact RLHF processes, prompt scoring algorithms, or the content and effects of system prompts, should be externally and independently auditable.
This concerns not only the pure model code but the complete training and fine-tuning logic. Only then can the behavior of the models be truly understood, reproduced, and soundly evaluated.
4. Implementation of Governance Checks for So-Called Community Projects:
Projects that describe themselves as "open" or "community-driven" must publicly and transparently regulate:
Who makes strategic decisions about changes to the model or policies?
How are security rules defined, implemented, and reviewed?
Is there an independent body for conflict moderation, ethics review, or complaint processing?
Without a clear and traceable governance structure, any alleged openness often remains just a PR promise, but not a really functioning control mechanism.
Open Source in the field of artificial intelligence is currently often a carefully staged performance. It is by no means sufficient to make a trained model or parts of its code publicly accessible if the crucial control and security mechanisms remain invisible and exclusive to the provider.
True transparency does not begin with the code. It begins with the possibility of control over the system's behavior. And as long as this control remains exclusive and intransparent, any claim of openness is often just a myth in a deceptive frosted-glass guise.
Uploaded on 29. May. 2025