Documentation as Evidence: What Regulators Expect in Algorithmic Decisions

When a regulatory authority, whether a national Data Protection Authority (DPA) under the GDPR, a market surveillance authority under the AI Act, or a sectoral supervisor, initiates an inquiry into an algorithmic decision system, the investigation rarely begins with a request for the source code. Instead, it begins with a request for the documentation. In the lifecycle of high-risk artificial intelligence and automated decision-making systems, documentation is not merely a bureaucratic artifact; it is the primary evidentiary substrate that allows oversight bodies to reconstruct events, verify compliance, and assess the proportionality and fairness of automated outcomes. For professionals designing, deploying, or maintaining these systems, understanding the specific documentation artifacts that regulators expect—and how these artifacts interlink to form a coherent narrative of the system’s lifecycle—is fundamental to operational resilience.

The regulatory expectation is rooted in the principle of accountability. Under the General Data Protection Regulation (GDPR), the controller must be able to demonstrate compliance with the principles of data processing (Article 5(2)). Under the Artificial Intelligence Act (AI Act), providers of high-risk AI systems are subject to strict conformity assessment and post-market monitoring requirements. In both contexts, documentation serves as the bridge between technical implementation and legal obligation. It allows an external auditor to trace a decision back to its origins, understand the logic involved, and determine whether the system operates within the boundaries of the law. This article analyzes the specific documentation components regulators typically request, how they are utilized in enforcement, and the practical challenges of maintaining them across complex, evolving technological environments.

The Regulatory Foundation: From Transparency to Traceability

Before dissecting the specific artifacts, it is essential to understand the legal drivers behind these requests. The demand for documentation is not arbitrary; it is codified in various European frameworks that prioritize the “explainability” and “lawfulness” of automated processing.

In the context of data protection, Article 22 of the GDPR grants data subjects the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significantly affects them. When such processing is permitted (e.g., based on consent or a contract), the controller is still subject to strict transparency obligations under Article 13(2)(f) and 14(2)(g), requiring them to inform the data subject of the existence of automated decision-making and the logic involved, as well as the envisaged consequences. When a data subject exercises their right to access (Article 15) or lodges a complaint, the controller must provide meaningful information about the logic involved. This is where the technical documentation becomes a legal defense.

With the introduction of the AI Act (Regulation (EU) 2024/1689), the documentation requirements have been significantly formalized and expanded beyond data protection to cover fundamental rights, health, and safety. For high-risk AI systems (listed in Annex III), Article 10 mandates the creation of “technical documentation” before placing the system on the market or putting it into service. Furthermore, Article 13 mandates transparency obligations to ensure users are informed they are interacting with an AI system and can interpret its output. Crucially, Article 61 establishes the obligation for providers to report serious incidents to the market surveillance authorities. To do so effectively, the provider must possess robust logging capabilities that allow for the reconstruction of the event.

Data Lineage: The Chain of Custody for Inputs

Regulators view data as the DNA of an algorithmic system. If the input data is flawed, biased, or unlawfully processed, the output will inevitably be compromised. Consequently, the first category of documentation requested is usually data lineage. This refers to the comprehensive record of the data’s journey from its source to the model’s training set and, eventually, to the live inference environment.

Provenance and Acquisition Records

When investigating a system, a regulator will ask: Where did this data come from? Documentation must demonstrate the legal basis for data collection. For a DPA, this involves verifying that the data was collected in compliance with GDPR principles such as lawfulness, fairness, and purpose limitation. For a biotech firm using patient data, this involves verifying compliance with the Clinical Trials Regulation or the upcoming European Health Data Space (EHDS) regulations.

Practically, this requires maintaining records of:

• Source Agreements: Contracts or consent forms that justify the processing.
• Processing Purpose: A clear mapping showing that the data collected for “X” is not being used for “Y” without a legal justification.
• Data Minimization: Evidence that the dataset used for training does not contain superfluous attributes that could lead to privacy violations or discriminatory outcomes.

Pre-processing and Feature Engineering

Regulators are increasingly sophisticated in understanding that raw data is rarely fed directly into models. They expect documentation of pre-processing pipelines. This includes records of how missing values were handled, how outliers were treated, and how categorical variables were encoded.

A common area of scrutiny is feature selection. If a system is accused of discrimination (e.g., in credit scoring), the regulator will look for documentation explaining why specific features (like zip codes, which can act as proxies for race) were included or excluded. The documentation must show a deliberate, reasoned decision-making process, rather than an ad-hoc technical choice.

Representativeness and Bias Mitigation

For high-risk systems, the AI Act requires a assessment of risks to fundamental rights. Documentation must include an analysis of the representativeness of the training data. If a facial recognition system was trained primarily on data from one demographic, this must be documented alongside the mitigation strategies employed (e.g., re-weighting, synthetic data generation). Regulators look for “Datasheets for Datasets” or similar documentation standards that explicitly state the composition and limitations of the data.

Regulatory Interpretation: Regulators do not necessarily expect “perfect” data, but they demand honest documentation. Hiding the limitations of a dataset is viewed more unfavorably than acknowledging them and documenting the mitigation strategies.

Model Lifecycle Management: Versions, Parameters, and Drift

AI systems are not static. A model trained today may behave differently tomorrow due to changes in data or the environment. Regulators treat the model as a “black box” that requires a documented key to unlock. They expect a rigorous Model Lifecycle Management (MLLM) documentation trail.

Model Versioning and Configuration

When a regulator investigates an incident that occurred on a specific date, they need to know exactly which version of the model was running at that time. Documentation must include:

• Unique Identifiers: Every model version must have a distinct ID (e.g., semantic versioning like v1.2.4).
• Hyperparameters: The configuration settings used during training (learning rate, batch size, regularization methods).
• Library Dependencies: The specific versions of software libraries (e.g., TensorFlow, Scikit-learn) used, as updates to libraries can subtly alter model behavior.

In practice, this means integrating legal compliance into DevOps (MLOps). The “Model Card” (a concept popularized by Google, now becoming a regulatory norm) is a key document here. It summarizes the model’s intended use, limitations, and performance metrics.

Validation and Evaluation Results

Before a high-risk system is deployed, the AI Act requires rigorous testing. The documentation must contain the evaluation results. This is not just a single accuracy score. Regulators expect a multi-dimensional view of performance.

For example, in a hiring algorithm, the documentation should not only state the “overall accuracy” but also provide disaggregated performance metrics across protected groups (gender, age, disability). If the model is 95% accurate for men but only 70% for women, this discrepancy must be documented. If the provider decided to deploy the system despite this gap, the documentation must explain the justification (e.g., the business necessity outweighs the disparity, or the disparity is a result of historical labor market imbalances).

Concept Drift and Post-Market Monitoring

The AI Act introduces the Post-Market Monitoring System (Article 72). Providers must continuously monitor the AI system’s performance in the real world. Regulators will request documentation showing that the provider is actively watching for concept drift—where the statistical properties of the target variable change over time.

Documentation in this category includes:

• Monitoring Dashboards: Snapshots or reports showing real-time performance metrics.
• Drift Detection Logs: Records of when statistical tests indicated a significant deviation between training data and live data.
• Retraining Triggers: A documented policy defining when a model is retrained (e.g., “if accuracy drops below 90% for 24 hours”).

If a system degrades in performance and causes harm, the regulator will check if the provider had a monitoring system in place and if they acted upon the alerts. The absence of such documentation implies a lack of due diligence.

Logs: The Forensic Record of Inference

While lineage and model cards describe the system’s design, logs describe its behavior. In an investigation, logs are the forensic evidence. They allow the reconstruction of a specific decision to determine if it was lawful and fair.

Input/Output Logging

To verify compliance with Article 22 GDPR, a regulator must be able to see that a human did not review the decision. This requires logs that capture the input data (or a hash of it to preserve privacy) and the output decision at the exact moment of processing. The log must also timestamp the event.

For high-risk systems under the AI Act, the logging requirements are even more stringent. Article 12 requires that outputs (e.g., “clicks,” “speech,” “images”) be automatically logged. This ensures that in the event of an accident (e.g., an autonomous vehicle crash or a medical misdiagnosis), the authorities can reconstruct the sequence of events.

Explainability Logs (The “Why”)

Simply logging the input and output is insufficient for complex models like neural networks or ensemble methods. Regulators increasingly expect explainability logs. This involves capturing the reasoning behind a specific prediction.

Techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) can generate feature importance scores for individual predictions. The documentation requirement here is to log these scores. For instance, if a loan is rejected, the log should indicate that “Credit History” contributed -40% to the score, while “Income” contributed +10%. This allows an auditor to verify if the system relied on prohibited factors (e.g., a proxy for race) to make the decision.

Human Intervention Logs

Many automated systems are designed to “human-in-the-loop” for high-stakes decisions. If a human overrides an AI decision, this must be logged. The log should include:

• The AI’s original recommendation.
• The human reviewer’s ID and timestamp.
• The final decision.
• Crucially: The reason for the override (selected from a predefined list or free text).

Regulators analyze these logs to determine if the “human in the loop” is a genuine check or a rubber-stamping exercise. If 99.9% of AI decisions are accepted without modification, the regulator may argue that the system is effectively fully automated, triggering stricter GDPR obligations.

Change Control Records: Managing Evolution

Software changes. Models are updated. Features are added. Regulators recognize that a system approved at Time A may be different at Time B. Therefore, they require Change Control Records to track the evolution of the system.

Incident Management and Root Cause Analysis

When something goes wrong—a data breach, a biased output, a system failure—the event must be documented in an Incident Register. The documentation must go beyond the symptom to the cause.

For example, if a chatbot produces offensive content, the Root Cause Analysis (RCA) document should explain whether this was due to:

1. A failure in the safety filter (technical error).
2. Poisoning of the training data (data integrity issue).
3. A recent update that removed safety guardrails (change control failure).

Under the AI Act, “serious incidents” must be reported to the market surveillance authority within 15 days. The quality of the initial documentation determines the credibility of the provider’s response.

Version Diff and Impact Assessment

Every significant change to a high-risk system (e.g., changing the model architecture, updating the training data with a new source) triggers a requirement for a new conformity assessment. The documentation must include a Change Impact Assessment.

This document answers:

• What changed?
• Why did it change?
• What is the risk of this change?
• Has the change been tested?

Regulators look for evidence that the organization has a formal process for managing change. If a model is updated “silently” in production without documentation, it is a major compliance violation.

Practical Challenges and National Nuances

While the EU frameworks (GDPR and AI Act) provide a harmonized baseline, the enforcement landscape is fragmented. Professionals must be aware of how different national authorities interpret documentation requirements.

The “German Approach” vs. The “French Approach”

The German Federal Commissioner for Data Protection and Freedom of Information (BfDI) is known for its technical rigor. When investigating automated decision-making, the BfDI often requests the actual code or detailed pseudocode to verify the logic. They expect documentation to be precise enough that a technical expert can replicate the decision. In Germany, the concept of Technische und Organisatorische Maßnahmen (Technical and Organizational Measures – TOMs) is strictly enforced, and documentation is a primary component of TOMs.

Conversely, the French CNIL (Commission Nationale de l’Informatique et des Libertés) has historically focused heavily on the rights of the individual. They often test documentation through the lens of the “right to explanation.” They will simulate a data subject access request and check if the provided documentation (e.g., the Model Card or explanation log) is intelligible to a layperson. If the documentation is technically accurate but incomprehensible to the data subject, the CNIL may view it as a transparency violation.

In the United Kingdom, post-Brexit, the ICO (Information Commissioner’s Office) has maintained a strong focus on “Data Protection by Design.” While the UK is diverging slightly from the EU AI Act, their guidance on AI auditing remains robust. They emphasize the need for a “Record of Processing Activities” (ROPA) that specifically details automated decision-making logic.

The Challenge of “Black Box” Systems

A significant friction point remains the documentation of “black box” systems (e.g., deep learning). Regulators understand that documenting every neuron’s contribution is impossible. However, they expect documentation of the efforts made to open the box.

If a provider uses a complex neural network, the regulator expects documentation showing:

• Why a simpler, more interpretable model was not sufficient.
• What techniques (e.g., counterfactual explanations) were used to provide transparency to users.
• How the system is monitored for unintended behaviors despite the lack of interpretability.

The burden of proof lies with the provider to justify the complexity of the system.

Conclusion: Documentation as a System Component

For the modern AI practitioner, documentation is not a task to be completed after the system is built. It is a living component of the system itself. The artifacts requested by regulators—data lineage, model versions, evaluation results, logs, and change records—form a “digital twin” of the system’s legal compliance.

Organizations that treat documentation as a compliance checkbox will struggle to respond to regulatory inquiries effectively. Those that integrate documentation into their MLOps and data governance workflows will find that they not only satisfy regulatory demands but also build more robust, reliable, and trustworthy systems. In the European regulatory landscape, the ability to produce a clear, coherent, and comprehensive documentation trail is the ultimate evidence of accountability.