[Interview] Park Ji-hwan, CEO of ThinkForBL: “In AI commercialization, reliability will be the differentiator.
“Technologies like the wheel or electricity were not perfected at the moment of invention. They became transformative only after being refined, put into practical use, and proven effective. Artificial intelligence (AI) is no different—its performance and accuracy improve over time. Ultimately, the success of commercialization depends on who can make AI more reliable and consistently demonstrate that reliability through design, rules, and operation.”
This was the perspective shared by ThinkForBL CEO Park Ji-hwan in a recent interview. ThinkForBL is a technology company specializing in AI reliability and safety, pursuing an approach that integrates both knowledge and technology to ensure trustworthy AI.
“Reliable AI requires a realistic, practical approach.”
CEO Park explained that AI reliability is “the degree to which we can trust that AI will not harm human life, property, or basic rights,” adding that it includes “how confidently AI can operate in compliance with ethics and laws.”
He emphasized that AI reliability should be regarded as a technical responsibility on the supplier side, not as a mere declarative statement.
Supplier responsibilities include:
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Ethical requirements (prevention of discrimination, protection of human dignity)
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Technical requirements (data governance, risk-mitigation design, testing and verification, logging and traceability, operational monitoring)
“Reliability is ensured when these requirements are measured, verified, and implemented at an auditable level,” he said.
He also pointed out the need for a practical approach to AI errors such as hallucination. The goal of AI development is not to eliminate all errors but to design and manage tolerable risks so that significant harm does not occur. Park noted that this should be treated as an engineering issue, similar to practices in aviation or medical safety.
“This is generally referred to as defense-in-depth,” he added. “It means applying systems for prevention, early detection, isolation, and recovery to prevent damage from expanding. Evidence-based methods, uncertainty management, and multi-layered verification will continue to develop.”
“AI reliability is where knowledge and technology meet.”
Park explained that securing AI reliability requires the combination of knowledge systems (standards, methodologies, governance) and technical tools (diagnosis, measurement, monitoring).
Just as expertise and equipment together ensure safety in medical environments, AI reliability becomes effective when both knowledge and technology operate together.
ThinkForBL has developed a structured knowledge system across the entire AI lifecycle—from planning to operation—including:
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Impact assessment
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Risk management
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Data bias analysis
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Model robustness evaluation
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Human oversight mechanisms
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Safe-mode design
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Operational revalidation
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Monitoring governance
ThinkForBL also operates a technical platform that integrates education and diagnostic tools to put AI reliability into practice.
Its AI Tutor provides real-time multilingual education and Q&A to help developers and managers quickly absorb reliability-related knowledge.
The RE:IN data fidelity diagnostic service evaluates not only statistical bias but also scenario-based contextual bias related to real-world impact, enabling a more accurate assessment of AI confidence.
The company also introduced TRAIN (Trusted AI International Network), an initiative aimed at promoting international technical standards for AI reliability. TRAIN facilitates practical global exchanges through events such as the TRAINS international symposium and the TRY:TON working hackathon, helping translate ethical discussions into technical verification and operational processes.
“For a long time in Korea, AI reliability and ethics remained in moral discourse, while overseas, they evolved into practical technological areas,” Park said. “We recognized the need for international cooperation and saw that a private-sector-led network—free from diplomatic constraints—was particularly important for working-level collaboration.”
“TRAIN aims to overcome reliability challenges that are difficult for individual organizations to address alone,” he added. “By respecting each country’s context while aligning on common methods, we are working to create a cycle of connection, exchange, sharing, and cooperation that leads to practical outcomes.”
“Turning declarations into action requires experts and practical systems.”
Regarding institutional measures for AI reliability, Park acknowledged the government’s efforts to establish frameworks but emphasized the need for international alignment and field applicability. Since regulatory frameworks often follow market realities, he stated that continuous review and improvement through pilot applications are necessary.
He identified the lack of practical, actionable methods as the biggest gap.
“In many cases, we know what values to protect, but not how to protect them,” he said.
“To achieve genuine progress beyond declarations, we need a closed-loop structure—diagnosis, prescription, implementation, and re-evaluation,” Park explained. “It is difficult for companies to advance with simple diagnostic consulting alone.”
He added that practical support—such as on-site coaching vouchers and manpower development vouchers—is necessary.
“The bottleneck in Korea is skilled personnel. The path to developing them is clear: building an implementation infrastructure that links ethics to requirements, requirements to tasks, and tasks to education.”
“Developing people and technology for practical AI deployment.”
Park concluded by saying that ThinkForBL aims to develop both people (skilled professionals) and technology (verification, audit, and operational tools) so that reliable AI commercialization becomes a fundamental capability for the nation and industry.
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