AIにおける評価結果の報告を再考する
Ryan Burnell1, Wout Schellaert2, John Burden1,3
1Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.
まとめ
複雑な研究の理解は,集約された指標と詳細な結果へのアクセスが制限されています. これは科学の進歩とデータの透明性を制限している.
科学分野:
- データサイエンス
- 研究方法論
背景:
- 現在の研究報告は,しばしば集約された指標に依存しています.
- 詳細なデータへのアクセスの欠如は,徹底的な科学的評価を妨げています.
研究 の 目的:
- 集約メトリックによって課される制限を強調する.
- 研究成果へのアクセスを改善し,理解を深めることを提唱する.
主な方法:
- 現在の科学出版基準の分析
- 研究におけるデータアクセシビリティ政策の見直し
主要な成果:
- データセット内の重要な詳細と違いを隠しています.
- 原始的または詳細な結果へのアクセスの制限は,再現性と批判的評価を制限します.
結論:
- 総合的な指標に頼りすぎると 科学的な理解が妨げられます
- 詳細な研究成果へのアクセスを促進することは,科学的進歩にとって不可欠です.
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