インシデントのステータス
CSETv1 分類法のクラス
分類法の詳細Incident Number
102
Notes (special interest intangible harm)
Voice recognition software's performance varied depending on the speaker's regional accents and race. Speech recognition performed worse for African American vernacular.
Special Interest Intangible Harm
yes
Date of Incident Year
2020
CSETv1_Annotator-1 分類法のクラス
分類法の詳細Incident Number
102
AI Tangible Harm Level Notes
3.3 - Though an AI was involved, there is no tangible harm to link it to.
Special Interest Intangible Harm
yes
Date of Incident Year
2020
Date of Incident Month
3
Date of Incident Day
23
CSETv1_Annotator-2 分類法のクラス
分類法の詳細Incident Number
102
Notes (special interest intangible harm)
Voice recognition software's performance varied depending on the speaker's regional accents and race.
Special Interest Intangible Harm
yes
Date of Incident Year
2020
Date of Incident Month
03
Date of Incident Day
23
インシデントレポート
レポートタイムライン
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- 情報源として元のレポートを表示
- インターネットアーカイブでレポートを表示
Speech recognition systems have more trouble understanding black users’ voices than those of white users, according to a new Stanford study.
The researchers used voice recognition tools from Apple, Amazon, Google, IBM, and Microsoft to tran…
- 情報源として元のレポートを表示
- インターネットアーカイブでレポートを表示
Automated speech recognition (ASR) systems are now used in a variety of applications to convert spoken language to text, from virtual assistants, to closed captioning, to hands-free computing. By analyzing a large corpus of sociolinguistic …
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