Our AI Safety Framework
NAVER takes a human-centric approach to developing AI, and our aim is to help people benefit from AI by turning this technology into a daily tool.
Since introducing NAVER’s AI Principles in 2021, human-centered AI development has always been the focus of our efforts. By building on our AI Safety Framework, we hope to proactively address potential harms related to AI systems.
Our AI Safety Framework is designed to address societal concerns around AI safety. We identify, assess, and manage risks at all stages of AI systems operations, from development to deployment. Technological advances in AI have accelerated the shift toward safer AI globally, and our AI safety framework, too, will be continuously updated to keep up with changing environments.
NAVER has always endorsed diversity when launching new technologies and services to make connections genuinely worthwhile. We believe that developing people-centered AI must go hand in hand with respecting diversity. While being part of the international community that works toward safer AI, we’re also dedicated to approaching AI within the socio-technical context from the perspective of a local society.
Risk awareness
The potential harms of AI that many people voice concern over broadly fall into one of two categories: “loss of control” and “misuse” risks.
The former concerns the fear of losing control over AI systems as they become more sophisticated, while the latter refers to the possibility of people deliberately manipulating these systems to catastrophic effect. AI’s technological limitations are also a key point in discussions about trust and safety.
NAVER’s AI Safety Framework defines the first category of risk as AI systems causing severe disempowerment of the human species. By this definition, this loss of control risk goes far beyond the implications of current AI-enabled automation, which stems from the concern that AI systems could spiral out of human control at the pace they are advancing. At NAVER, we take this risk seriously as we continually apply our standards to look for signs of alarm.
Our AI Safety Framework describes the second risk category as misusing AI systems to develop hazardous biochemical weapons or otherwise use them against their original purpose. To mitigate such risks, we have to place appropriate safeguards around AI technology. NAVER has taken a wide range of technological and policy actions so far and will continue to work toward achieving AI safety.
Risk assessment and management
Our AI Safety Framework outlines specific actions for everyone at NAVER working in the field, for those who develop and deploy AI systems and are committed to our AI Principles. With this framework, we hope to address the “loss of control” and “misuse” risks.
We use the risk assessment scale for the former category and the risk assessment matrix for the latter to manage potential risks. When building AI systems, we try to do so in the socio-technical context, which means training and evaluating language models with local datasets within the proper cultural and societal context.
A. AI risk assessment scale
The risk assessment scale examines risks in the “loss of control” category to see whether they are positively correlated with the advancement of AI systems. LLMs should be subject to periodic reviews or assessed whenever major performance improvements are made.
Depending on their technological level, AIs can be divided into three types: hyper-scale, frontier, and future AI. For the purpose of our evaluation, we’ll focus on frontier AI. Future AI refers to AI systems that have yet to arrive and can only be evaluated once their capabilities become clearer.
| Hyper-scale AI | Frontier AI | Future AI | |
| Technological level | Hyper-scale AI systems | AI systems with the highest possible capabilities that currently exist or are to come in the next few years | AI systems of the future |
| Evaluation cycle | Non-existent | Every 3 months,or when performance
increases by 6x |
To be determined laterdepending on
their future capabilities |
Frontier AI possesses the top capabilities that are available today or will be soon in the near future. Our goal is to have AI systems evaluated quarterly to mitigate loss of control risks, but when performance is seen to have increased six times, they will be assessed even before the three-month term is up. Because the performance of AI systems usually increases as their size gets bigger, the amount of computing can serve as an indicator when measuring capabilities.
B. AI risk assessment matrix
For the other “misuse” risk category, we use a risk assessment matrix to manage risks.
| Use cases | Need for safety guardrails | |
| Assessing risks | – Determine whether an AI system designed to serve a certain purpose can cause potential harm in special use cases | – Collaborate with different teams to identify and calculate the probability of risks across the entire lifecycle |
| Managing risks | – For special use cases, make AI systems available only to authorized users- For general use cases, build guardrails by restricting special-use capabilities | – Delay deploying AI systems until risks are mitigated and appropriate technological and policy actions have been taken |
| Examples | – Special use case: biochemical weaponization | – Technological measure: AI safety updates on HyperCLOVA X models |
The AI risk assessment matrix is used to identify, evaluate, and manage risks during an AI system’s entire lifecycle on two grounds: its purpose or use case and the need for safety guardrails.
| Need for safety guardrails | |||
| Low | High | ||
| Use cases | General purpose | Low riskDeploy AI systems but perform monitoring afterward to manage risks | Risk identifiedWithhold deployment until additional safety measures are taken |
| Special purpose | Risk identifiedOpen AI systems only to authorized users to mitigate risks | High riskDo not deploy AI systems | |
Once AI systems are evaluated and their risks identified according to the two standards, we must implement appropriate guardrails around them. We should only deploy AI systems if those safeguards have proven effective in mitigating risks and keep an eye on the systems even after deployment through continuous monitoring. In theory, there may be cases where AI systems are used for special purposes and require safety guardrails in place, in which case AI systems should not be deployed.
| Need for safety guardrails | |||
| Low | High | ||
| Use cases | General purpose | Deploy AI systems only after implementing guardrails through technological and policy actions and risks have been sufficiently mitigated | |
| Special purpose | Ensure special-use capabilities are restricted for general use cases | ||
Keep special-use capabilities off limits for general purposes by placing appropriate safeguards. If safety guardrails are deemed necessary, take technological and policy actions to reduce risks and deploy only when risks are sufficiently mitigated.
We draw from our AI Principles and research studies to implement guardrails around AI models. Our partnerships with academia, the tech industry, and other stakeholders have led to meaningful research in building local datasets specialized to Korean culture and society and creating benchmarks for evaluating Korean-centric models.
We apply *SQuARe , KosBi , *KoBBQ datasets built from our research to our HyperCLOVA X models. We also make parts of these studies available to everyone as we continue to collaborate with various stakeholders in conducting safety research.
*SQuARe, KoSBi: https://github.com/naver-ai/korean-safety-benchmarks
*KoBBQ: https://huggingface.co/datasets/naver-ai/kobbq
AI governance
NAVER’s AI Safety Framework is our initiative to achieve AI governance. Under our governance, we foster collaboration between cross-functional teams to identify, evaluate, and manage risks when developing AI systems.
NAVER’s AI governance includes:
• The Future AI Center, which brings together different teams for discussions on the potential risks of AI systems at the field level
• The risk management working group whose role is to determine which of these issues to raise to the board
• The board (or the risk management committee) that makes the final decisions on the matter
External collaborations
We work with external stakeholders to take on challenges surrounding safe AI technologies and services.
Building trust in AI is a collective effort, which is why we partner with top universities like Seoul National University (SNU) and Korea Advanced Institute of Science & Technology (KAIST) on the technology front and participate in the SNU AI Policy Initiative on the policy front.
But it’s not just the experts we’re collaborating with. We also work with our users, improving AI through their feedback and writing guides on Using AI for People.
In April this year, we held our first Generative AI Red Teaming Challenge. Participants screened LLMs for signs of harmful content across seven domains—human rights violation, disinformation, inconsistency, cyberattacks, bias and discrimination, illegal content, and jailbreaking—exposing vulnerabilities by triggering the models to generate unethical responses that were biased or discriminatory.