言語コーポラから自動的に派生する意味論は,人間のようなバイアスを含んでいます
Aylin Caliskan1, Joanna J Bryson1,2, Arvind Narayanan1
1Center for Information Technology Policy, Princeton University, Princeton, NJ, USA. aylinc@princeton.edu jjb@alum.mit.edu arvindn@cs.princeton.edu.
Science (New York, N.Y.)
|April 15, 2017
まとめ
ウェブテキストで訓練された機械学習モデルは,暗黙の関連テストで発見された人間の意味論的バイアスを複製します. これは歴史的なバイアスが 言語データに埋め込まれていることを明らかにし テクノロジーにおける 文化的バイアスを特定し 解決する方法を提示しています
科学分野:
- 人工知能
- 自然言語処理
- コンピュータ社会科学
背景:
- 機械学習 (ML) は,データのパターンを特定することによって人工知能を導きます.
- 人間の言語の体には 隠された社会的バイアスが含まれています
- 暗黙の関連テスト (IAT) は,概念間の自動関連の強さを測定します.
研究 の 目的:
- 人間の言語で訓練された機械学習モデルが 人間のような意味学的なバイアスを示すかどうかを調査する.
- MLモデルがIATで測定されたバイアスを再現できるかどうかを判断する.
- 文化的なバイアスを特定し緩和するMLの可能性を探求する.
主な方法:
- 統計的な機械学習モデルを ワールドワイドウェブからの大量のテキストに適用した.
- 標準のテキストデータでモデルを訓練した.
- IATで測定したものを含め,既知の人間のバイアスに対してモデルの意味学的な関連性を評価した.
主要な成果:
- MLモデルは人間の意味学的なバイアスのスペクトルを複製し,IATの結果を反映した.
- バイアスは,道徳的に中立 (昆虫,花),問題 (人種,性別),真実 (性別,キャリア/名前) などの様々な領域で観察されました.
- テキスト・コーポラは ML分析によって 回復可能な 歴史的な人間のバイアスを正確に 刻印します
結論:
- リアルなテキストデータで訓練された機械学習モデルは 人間の意味学的なバイアスを継承し 反映しています
- テキストデータは,MLを使用して定量化できる歴史的バイアスのリポジトリとして機能します.
- 開発された方法は,文化的および技術的なバイアスを検出し対処するための有望なアプローチを提供します.
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