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AI incident #934 ·

NHK Terminates AI Translation Service After Geopolitical Naming Error

Open on the AI Incident Database 3 news reports Snapshot 7 Sep 2026

What happened

NHK announced the termination of its AI-powered multilingual subtitle service after an automatic translation error rendered "Senkaku Islands" as "Diaoyu Islands," the Chinese designation. The mistake, discovered on February 10th, 2025 during a news segment, led NHK to deem the service inappropriate. The AI-based subtitles, powered by Google Translate, had been in use since 2020.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (3)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. NHK　多言語字幕サービス終了へ　英語によるライブ配信の字幕にミス「尖閣諸島」を「釣魚島」と表記
    news.yahoo.co.jp · Sponichi Annex · AIID #4703
  2. ＮＨＫ、ＡＩの多言語字幕サービス終了　尖閣諸島を中国名で表示のため
    jiji.com · 時事通信 文化特信部 · AIID #4704
  3. Senkaku Islands Referred to as Diaoyu Islands in Subtitles of NHK Report; Japanese Broadcaster Says AI System Responsible
    japannews.yomiuri.co.jp · The Yomiuri Shimbun · AIID #5293

Who was involved

Alleged deployer
Nhk
Alleged developer
Google
Alleged harmed party
Nhk, Government Of Japan, Nhk Viewers, Public Trust In The Media, Japan China Relations

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 3.1.

  • Pursuing Consistent Context

    "LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstra...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Knowledge Gaps

    "Since the training corpora of LLMs can not contain all possible world knowledge [114]–[119], and it is challenging for LLMs to grasp the long-tail knowledge within their training data [120], [121], LLMs inherently posse...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Hallucinations

    "LLMs generate nonsensical, untruthful, and factual incorrect content"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Faithfulness Errors

    "The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Defective Decoding Process

    In general, LLMs employ the Transformer architecture [32] and generate content in an autoregressive manner, where the prediction of the next token is conditioned on the previously generated token sequence. Such a scheme...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Untruthful Content

    "The LLM-generated content could contain inaccurate information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Factuality Errors

    "The LLM-generated content could contain inaccurate information" which is factually incorrect

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Noisy Training Data

    "Another important source of hallucinations is the noise in training data, which introduces errors in the knowledge stored in model parameters [111]–[113]. Generally, the training data inherently harbors misinformation....

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Incidents in the same risk subdomain

All incidents in this subdomain