A mountain of public opinions that once took government officials over a month to sort through, processed by AI in just 10 minutes. Fujitsu's large language model "Takane" has successfully automated the classification and summarization of approximately 120,000 characters of public comment data in a proof-of-concept with a Japanese central government agency. Japan's government AI adoption is shifting from "exploration" to "real-world deployment."

What Are Public Comments? The Bridge Between Government and Citizens

In Japan, the public comment system (formally known as the "Opinion Solicitation Procedure" under the Administrative Procedure Act) requires government agencies to publicly disclose draft regulations, such as cabinet orders and ministerial ordinances, and solicit opinions from the general public before finalizing them. It serves as a critical mechanism for ensuring transparency and fairness in policymaking.

However, the system has long faced operational challenges. Topics of high public interest can generate thousands or even tens of thousands of submissions. Government officials must read every opinion, classify them by topic and stance, draft individual responses, and consider how to reflect them in policy. The entire process often takes over a month before results are published.

What Fujitsu's "Takane" Actually Achieved

On February 3, 2026, Fujitsu announced the results of a demonstration experiment conducted in collaboration with a central government agency, applying its LLM "Takane" to public comment operations.

Takane is an enterprise-grade LLM co-developed by Fujitsu and Canadian AI company Cohere. It has achieved world-leading performance on the JGLUE (Japanese General Language Understanding Evaluation) benchmark and is designed for use in secure private environments, critical for sectors like government, finance, and R&D where data confidentiality is paramount.

The proof-of-concept, conducted in 2025 using actual past public comment data (approximately 120,000 characters) from the participating agency, yielded the following results:

  • Automated classification and summarization: Tasks such as sorting opinions by support or opposition and generating summaries, previously done manually, were completed in approximately 10 minutes
  • Cross-referencing with draft legislation: When both the draft law and individual opinions were fed into Takane, the system correctly identified the relevant legal clauses for over 80% of the submitted opinions
  • Freeing officials for higher-value work: By reducing time spent on organizing and tabulating opinions, officials can redirect their efforts toward substantive tasks like evaluating opinion content and incorporating feedback into policy

Alignment with Japan's Digital Agency Initiatives

This experiment is part of a broader movement driven by Japan's Digital Agency, which has identified public comment aggregation and analysis as a key area for generative AI application. The initiative also ties into EBPM (Evidence-Based Policy Making) efforts, where efficient digital analysis of citizen feedback is seen as essential for improving policy quality.

Building on these results, Fujitsu is developing a generative AI service that goes beyond public comment processing to support the entire spectrum of policy formulation and legislative drafting, with a target launch by fiscal year 2026. Future plans include constructing AI workflows that systematically integrate appropriate AI models throughout the legislative process, as well as developing AI agents capable of autonomously supporting complex research and coordination tasks.

Why a Japanese-Specialized LLM Matters

Most leading LLMs have been developed primarily for English. Japanese, however, presents unique challenges: mixed character systems (kanji, hiragana, katakana), frequent subject omission, and complex honorific expressions. Processing public comments that contain administrative jargon and legal terminology demands strong Japanese contextual understanding.

There is also a practical constraint: general-purpose cloud-based LLMs are often unsuitable for handling sensitive government data that cannot leave secure environments. Takane's design for private deployment gives it a significant advantage in government adoption scenarios.

Does AI-Powered Public Comment Processing Strengthen Democracy?

The original purpose of the public comment system is to reflect diverse citizen voices in policymaking. In practice, however, the sheer volume of submissions can overwhelm officials, leading to concerns about the system becoming a formality rather than a genuine feedback mechanism.

If AI-driven classification and summarization become standard practice, officials could be freed from organizational busywork to focus on genuinely engaging with the substance of public opinion. There is also the potential for AI to surface minority viewpoints and novel perspectives that might otherwise be buried in massive datasets, potentially improving the quality of democratic processes.

At the same time, there are legitimate concerns. AI summarization could strip away nuance from individual opinions. Classification biases could subtly influence policy decisions. The principle that final policy judgments must remain with human officials is essential, with AI serving strictly as a support tool.

Japan is steadily advancing government digitalization and AI adoption. Does your country use AI technology when gathering citizen input on policy? How do public participation mechanisms like public comment periods work where you live? We'd love to hear your perspective.

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