Description: Researchers from Boston University and Microsoft Research, New England demonstrated gender bias in the most common techniques used to embed words for natural language processing (NLP).
推定: Microsoft Research , Boston University と Googleが開発し、Microsoft Research と Boston Universityが提供したAIシステムで、Women と Minority Groupsに影響を与えた
インシデントのステータス
インシデントID
12
レポート数
1
インシデント発生日
2016-07-21
エディタ
Sean McGregor
Applied Taxonomies
CSETv0 分類法のクラス
分類法の詳細Public Sector Deployment
"Yes" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). "No" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.
No
Lives Lost
Were human lives lost as a result of the incident?
No
Intent
Was the incident an accident, intentional, or is the intent unclear?
Unclear
Near Miss
Was harm caused, or was it a near miss?
Unclear/unknown
Ending Date
The date the incident ended.
2016-01-01T00:00:00.000Z
Beginning Date
The date the incident began.
2016-01-01T00:00:00.000Z
CSETv1 分類法のクラス
分類法の詳細Incident Number
The number of the incident in the AI Incident Database.
12
CSETv1_Annotator-1 分類法のクラス
分類法の詳細Incident Number
The number of the incident in the AI Incident Database.
12
CSETv1_Annotator-3 分類法のクラス
分類法の詳細Incident Number
The number of the incident in the AI Incident Database.
12
Notes (special interest intangible harm)
Input any notes that may help explain your answers.
gender bias
Special Interest Intangible Harm
An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.
yes
Date of Incident Year
The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.
Enter in the format of YYYY
2016
Date of Incident Month
The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.
Enter in the format of MM
7
Date of Incident Day
The day on which the incident occurred. If a precise date is unavailable, leave blank.
Enter in the format of DD
21
インシデントレポート
レポートタイムライン
arxiv.org · 2016
- 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
The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning…
バリアント
「バリアント」は既存のAIインシデントと同じ原因要素を共有し、同様な被害を引き起こし、同じ知的システムを含んだインシデントです。バリアントは完全に独立したインシデントとしてインデックスするのではなく、データベースに最初に投稿された同様なインシデントの元にインシデントのバリエーションとして一覧します。インシデントデータベースの他の投稿タイプとは違い、バリアントではインシデントデータベース以外の根拠のレポートは要求されません。詳細についてはこの研究論文を参照してください
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