Human Oversight — Art. 14 Measures & Controls
Article 14 of the EU AI Act requires that high-risk AI systems be designed and developed in such a way that they can be effectively overseen by natural persons during their period of use. The Human Oversight module in Venvera provides a structured registry for documenting oversight measures, transparency notices, override procedures, monitoring protocols, and bias safeguards. Each record links to a specific AI system and includes details about the overseer, review schedules, and capability verifications. This article covers every aspect of the module.
Why Human Oversight Matters
Human oversight is one of the most distinctive requirements of the EU AI Act. It reflects the regulation's core principle that AI systems should augment human decision-making, not replace it entirely — particularly in high-risk contexts where the decisions affect people's fundamental rights, health, or safety. Art. 14 requires that oversight measures be proportionate to the risks of the AI system and be implemented through human-in-the-loop, human-on-the-loop, or human-in-command approaches. The goal is to ensure that a competent human can always understand, monitor, and if necessary override or stop the AI system.
List View
Step 1 — Open the Human Oversight List
Navigate to EU AI Act → Human Oversight from the sidebar. The list view shows all oversight records across all AI systems, sorted by last-updated date. Each row displays the record title, linked AI system, type badge, status, overseer name, and next review date.
Step 2 — Search Records
Use the search bar to filter records by title, overseer name, or AI system name. The search supports partial, case-insensitive matching. This is useful for finding all oversight records assigned to a specific person or related to a specific AI system.
Step 3 — Filter by Status
Use the Status dropdown to filter records by their current status. Statuses typically include Active, Under Review, Draft, and Expired. Active measures are currently in force. Under Review measures are being reassessed. Draft measures are being developed but not yet implemented. Expired measures have passed their review interval without being renewed.
Step 4 — Filter by Type
Use the Type dropdown to filter by the specific type of oversight record. This helps when you want to review all records of a specific category across your entire AI portfolio.
Record Types
The Human Oversight module supports five distinct record types, each addressing a different aspect of Art. 14 compliance:
| Type | Art. 14 Reference | Description |
|---|---|---|
| Human Oversight | Art. 14(1)-(2) | A general human oversight measure defining who oversees the AI system, what they monitor, how they intervene, and the frequency of their oversight activities. This is the primary record type and directly addresses the core requirement that high-risk AI systems be effectively overseen by natural persons. Each high-risk AI system should have at least one Human Oversight record documenting the oversight framework. The record should specify whether the oversight approach is human-in-the-loop (human approves each decision), human-on-the-loop (human monitors and can intervene), or human-in-command (human has overall authority and can override at any time). |
| Transparency Notice | Art. 14(4)(a) | A record documenting the transparency information provided to persons subject to the AI system's decisions. Art. 14(4)(a) requires that oversight measures enable the overseer to fully understand the capabilities and limitations of the AI system. Transparency notices document what information is disclosed, to whom, in what format, and at what point in the process. Examples include notices informing job applicants that AI is used in CV screening, or notices informing insurance applicants that AI contributes to risk assessment. The record should include the actual text of the notice or a link to where it is published. |
| Override Procedure | Art. 14(4)(d)-(e) | A documented procedure enabling the human overseer to decide not to use the AI system's output, to override or reverse the AI system's output, or to intervene in the operation or interrupt the system through a stop mechanism. Override procedures are critical for demonstrating that the human remains in ultimate control. The record should specify the exact steps the overseer must take to override a decision, the technical mechanism for doing so (e.g., a manual override button, an escalation workflow, a system shutdown command), the conditions under which an override should be exercised, and the documentation required when an override occurs. |
| Monitoring Protocol | Art. 14(4)(b)-(c) | A protocol defining the ongoing monitoring activities that the human overseer performs to ensure the AI system continues to operate within its intended parameters. This includes monitoring for anomalous outputs, data drift, performance degradation, and emerging biases. The protocol should specify what metrics are monitored, how frequently they are reviewed, what thresholds trigger investigation or intervention, and how monitoring findings are documented and escalated. Monitoring protocols complement the post-market monitoring plans in the Monitoring module but focus specifically on the human oversight dimension. |
| Bias Safeguard | Art. 14(2) | A specific safeguard measure designed to prevent, detect, or mitigate bias in the AI system's operation. While bias detection at the data level is covered in the Data Governance module, bias safeguards in the Human Oversight module focus on operational biases — i.e., biases that emerge during the system's use in production, potentially due to changing population characteristics, feedback loops, or interaction effects. The record should describe the bias risk being addressed, the safeguard mechanism (e.g., demographic parity monitoring, fairness dashboard, periodic manual audit of decisions), and the corrective actions triggered when bias is detected. |
Creating a New Record
Click + Add Oversight Measure to open the creation form. Complete all required fields:
| Field | Type | Required | Description |
|---|---|---|---|
| Title | Text input | Required | A descriptive title for the oversight measure (e.g., "Credit Risk Model — Human-in-the-Loop Decision Review", "Chatbot Transparency Notice — Customer Service", "Emergency Override Procedure — Automated Triage System"). Maximum 300 characters. The title should clearly convey the type of measure and the AI system it applies to. |
| AI System | Dropdown | Required | Select the AI system this oversight measure applies to. The dropdown lists all AI systems in your inventory. Each oversight measure must be linked to exactly one AI system. If the same oversight procedure applies to multiple systems, create separate records for each to maintain clear traceability. |
| Type | Dropdown | Optional | Select the type of oversight record from the five options described above: Human Oversight, Transparency Notice, Override Procedure, Monitoring Protocol, or Bias Safeguard. Choosing the correct type ensures proper categorisation and supports completeness analysis across your AI portfolio. |
| Description | Rich text editor | Optional | A detailed description of the oversight measure. This is the main body of the record and should be as comprehensive as necessary to fully document the measure. For Human Oversight records, describe the oversight approach, scope, and activities. For Override Procedures, include step-by-step instructions. For Transparency Notices, include the full notice text. For Monitoring Protocols, include metrics, thresholds, and escalation procedures. For Bias Safeguards, include the bias risk, detection method, and mitigation actions. The rich text editor supports formatting, lists, tables, and links. No character limit. |
| Overseer Name | Text input | Optional | The name of the person designated as the human overseer for this measure. This should be a specific individual, not a role title (though the role should be recorded separately). Art. 14 emphasises that oversight must be performed by natural persons who have the necessary competence, training, and authority. If the oversight responsibility rotates among team members, enter the team lead's name and document the rotation schedule in the description. |
| Overseer Role | Text input | Optional | The organisational role or title of the overseer (e.g., "Senior Data Scientist", "Compliance Officer", "Head of AI Ethics", "Operations Manager"). The role provides context about the overseer's qualifications and authority level. Art. 14(4)(a) requires that the overseer fully understand the AI system's capabilities and limitations, which implies a certain level of technical or domain expertise. |
| Review Interval (days) | Number input | Optional | The number of days between scheduled reviews of this oversight measure. For example, entering 90 means the measure should be reviewed every 90 days. When the review interval elapses, the record's status automatically changes to "Under Review" (or a notification is generated, depending on your configuration), and the record appears in the "Needing Review" count on the dashboard. Recommended intervals: 30 days for critical high-risk systems, 90 days for standard high-risk systems, 180 days for limited-risk systems. |
| Competency Verified | Checkbox | Optional | Check this box to confirm that the designated overseer has the necessary competence, training, and authority to effectively oversee the AI system as required by Art. 14. Competency verification should include: (a) understanding of the AI system's intended purpose, capabilities, and limitations; (b) ability to correctly interpret the system's output; (c) knowledge of when and how to exercise the override mechanism; (d) awareness of potential biases and the conditions that may trigger them. Document the specific training or qualifications in the description field. Unchecked boxes are flagged in the "Needing Review" count as a gap that requires attention. |
| Override Capability | Checkbox | Optional | Check this box to confirm that a technical mechanism exists enabling the overseer to override, disregard, or reverse the AI system's output, or to interrupt the system's operation. Art. 14(4)(d)-(e) specifically requires that the overseer be able to "decide, in any particular situation, not to use the high-risk AI system or to otherwise disregard, override or reverse the output" and to "intervene in the operation of the high-risk AI system or interrupt the system through a 'stop' button or a similar procedure." If this box is unchecked, a warning is displayed recommending immediate implementation of override capabilities for high-risk systems. |
Art. 14 Compliance Checklist: For each high-risk AI system, ensure you have documented at minimum: (1) A Human Oversight record defining the oversight framework and approach; (2) An Override Procedure detailing how the overseer can intervene; (3) A Monitoring Protocol specifying ongoing monitoring activities; and (4) A Transparency Notice if the system interacts with or affects natural persons. Bias Safeguards are recommended for any system that processes data about natural persons.
Tip — Linking to Training Records: The competency verification checkbox indicates whether the overseer is qualified, but it does not capture the details of their training. Consider maintaining a separate training log (either in the description field or in an external system) that records: the training programme completed, the date of completion, the trainer, and the topics covered. This evidence is invaluable during regulatory audits. Regular refresher training is recommended — update the competency verification and review interval accordingly.
Warning — Automation Bias: One of the key risks that human oversight is designed to mitigate is "automation bias" — the tendency for human overseers to over-rely on the AI system's output and fail to exercise independent judgement. Your oversight measures should actively counter this risk. Consider including in your oversight procedures: mandatory review of a random sample of AI decisions (not just flagged ones), periodic "challenge" exercises where overseers are asked to identify deliberately incorrect outputs, and training on the psychological factors that contribute to automation bias. Document these countermeasures in the description field of the relevant oversight record.