犯罪再発率をリアルタイムで予測するための公平性スケール
Jacob Verrey1, Peter Neyroud1, Lawrence Sherman1,2
1Institute of Criminology, University of Cambridge, Sidgwick Ave, Cambridge, CB3 9DA UK.
Neural computing & applications
|September 4, 2025
まとめ
機械学習モデルは 再犯を正確に予測し 既存の方法を上回ります 刑事司法と犯罪の削減に偏ったアプローチを提供することで 人口統計学的な公平性を実現します
科学分野:
- 刑事司法
- 機械学習
- データサイエンス
背景:
- 犯罪の再発を予測することは 資源の配分と公衆の安全にとって極めて重要です
- 既存の予測モデルは 社会的バイアスを永続させ 不公平な結果につながります
- 警察国家コンピュータ (PNC) のデータセットは,再犯研究のための大規模な基盤を提供します.
研究 の 目的:
- 一般的および暴力的な再犯を予測するための機械学習モデルを開発し評価する.
- これらの予測モデル内の社会的バイアスを評価し,軽減します.
- 公正で効果的な再犯予測ツールの導入のための枠組みを提案する.
主な方法:
- イギリス警察の全国コンピュータの有罪判決データ (346,685件の記録) を使って12の機械学習モデルを生成した.
- カーブ下の面積 (AUC) スコアに焦点を当てたモデル評価の5倍クロス検証を使用しました.
- モデル予測における人口格差を定量化して対処するための新しい公平性スケールを開発しました.
主要な成果:
- 最良のモデルはAUCスコア0. 8660 (一般) と0. 8375 (暴力的な再犯) を達成し,最先端を上回った.
- 偏差のないモデルでは 公平性の定義が5つとも満たされ 人口統計学的差は1%以内でした
- 構造的なバイアスの影響が減る可能性があることが示されています.
結論:
- 機械学習は 再犯を効果的に予測し 公平性を大幅に改善します
- 開発された公平性のスケールは,刑事司法モデルをデバイズするための貴重なツールを提供します.
- 提案された導入には,保護措置とランダム化制御試験が 犯罪と偏見を減らすための実用的な効果を検証する.
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