Feedback loop
A control architecture in which the current output (measured angle) is compared to the desired output (commanded angle) and the difference (error) is used to generate a corrective control signal. Feedback allows the system to reject disturbances and maintain position accuracy against external forces.
Defined in 4 GAGE programs, which carry 4 distinct definitions of it. The wording above is taught in Foundations of Robotics and Physical AI.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A control architecture in which the current output (measured angle) is compared to the desired output (commanded angle) and the difference (error) is used to generate a corrective control signal. Feedback allows the system to reject disturbances and maintain position accuracy against external forces.
The cycle that turns a process's real-world outcomes into improvement: capture what happened (especially human corrections), analyze for patterns, improve the process, and verify the improvement. The offensive half of governance, versus monitoring's defense.
A dynamic in which the outputs of an AI system influence the future training data for that system, causing any bias in current outputs to be amplified in subsequent training cycles. Explicitly addressed in Article 10(2)(f) and Recital 67.
A structure in which a system's output, directly or indirectly, becomes one of its own future inputs. Feedback loops determine how a system responds to a disturbance over time.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Process Governance and Continuous Improvement · Process and Operations Redesign, Certified AI Transformation Professional (CATP)
- Data Governance and Dataset Documentation (Article 10) · High-Risk AI Requirements: The Technical File, EU AI Act Implementation Expert
- Servo motors and closed-loop position control · Actuators and Mechanisms, Foundations of Robotics and Physical AI
- Thinking in systems, not isolated parts · Engineering Thinking and Elegant Minimalism, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Feedback loop starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Profiles data, sets rules and thresholds, monitors, assigns issues to owners, and knows when data is not fit to train or run a model.
One lesson on this page teach it as a primary outcome.
Designs decision rights, committees, intake, approval tiers and escalation so routine uses move and consequential uses get reviewed.
One lesson on this page teach it as a primary outcome.
Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Chief Data Officer, data leadership editionneeds it at depth
- Data Governance Leadneeds it at depth · 2 open postings this week
- AI Model Validatorneeds working knowledge
- Chief AI Officerneeds it at depth
- Chief Compliance Officer, AI compliance editionneeds it at depth
- VP of AI Governance and Ethicsneeds it at depth
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act Article 10; ISO 8000; NIST AI RMF GOVERN; ISO/IEC 42001 Clause 5.
One interview question about it
Which data quality rules would you monitor for a model in production, and what happens when one fails?
Asked of candidates for data quality rules and monitoring. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Data analysis, stewardship and business intelligence carries it as work you can show: Quality rules and profiling results are your artifacts.
- A data science, statistics or analytics degree carries it as a claim to back up: Data quality rules are familiar; monitoring them in production is new.
- Model risk, credit risk and quantitative analysis carries it as a claim to back up: Performance monitoring thresholds are familiar.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.