I can't explain the results.
AI becomes difficult to trust in audit, reporting, and complaint-handling situations.
Strange results occur only in specific situations.
Bias builds up quietly. By the time it’s discovered, the problem has already occurred.
Unable to track what changed.
Even if the results change after a model update, there’s no way to find the cause.
AI Reliability
We put recognized AI safety principles into practice.
Based on the AI reliability standards of the Telecommunications Technology Association (TTA) of Korea, we apply proven principles to all products and services from design through operation.
Even if AI makes the judgment, the responsibility for the final decision lies with people.
Governance Committee
Assign person in charge
Mandatory approval process
It is designed so that the same conditions always produce the same result.
Model Card
Data documentation
Log-based reproducibility verification
Provides the basis for judgment and influencing factors along with the results.
Output score + supporting text
Pre-disclosure of evaluation criteria
Design it so that a person can intervene and stop it when a problem occurs.
Real-time monitoring
Reporting Channel
Discontinue use if abnormalities occur.
All changes can be recorded in history and tracked with an approval process.
Change Approval Process
Document retention
Periodic revalidation
Agents are managed according to agent-specific criteria.
Delegation scope control
Orchestration tracking
Decision Path Audit
AI Ethics
Ethics is not a declaration,
it is a managed system.
Based on the ethics checklist developed with the government agency KISDI,
we manage the entire process from design to operation.
01
Human rights protection
We respect users as individuals and do not discriminate for any reason.
02
Privacy protection
We do not collect unnecessary personal information, and if requested, we will destroy it according to the proper procedures.
03
Respect for Diversity
Recognize the possibility of bias in AI models and take control measures to ensure fairness.
04
Infringement prohibited
Various experts participate in labeling, and bias is minimized through training, consensus, and verification.
05
publicness
We support users’ rational decision-making and continuously review social impacts.
06
Solidarity
We reflect stakeholders’ opinions and work closely with external organizations and government agencies.
07
Data management
We establish systematic data management measures and destroy the data according to procedures once its purpose has been fulfilled.
08
Accountability
Designate a person responsible for compliance with ethical standards and establish accountability for errors and procedures for improvement.
09
Safety
If an error occurs, we will notify you immediately and take prompt follow-up action.
10
Transparency
We inform users in advance about the limitations and scope of AI use, and respond sincerely to requests for explanations.