On Formalizing Fairness in Prediction with Machine Learning
公平的定義和衡量標準 文獻中提出了幾種公平的定義，但還沒有形成明確的共識。然而，這些定義可以用作在ML測試中檢測違反公平性的預言。 爲了幫助說明ML公平性的形式化，我們使用X表示一組個體，Y表示在對X中的每個個體進行決策時的真實標籤集。設h是訓練好的機器學習預測模型。設A爲敏感屬性集，Z爲剩餘屬性。 1)Fairness Through Unawareness 無意識公平。無意識公平(FT
相關文章
- 1. hand on machine learning with
- 2. [GPU] Machine Learning on C++
- 3. Chapter 9 On-policy Prediction with Approximation
- 4. 【機器學習中的公平性】Fairness in Machine Learning
- 5. Tackling Climate Change with Machine Learning
- 6. Machine and Deep Learning with Python
- 7. An Introduction to Machine Learning with Python
- 8. Fixed error when submitting assignments in Machine Learning on Coursera
- 9. 論文解讀：Multimodal Machine Translation with Embedding Prediction
- 10. Classification and inference with machine learning
- 更多相關文章...
- • Docker Machine - Docker教程
- • SQL IN 操作符 - SQL 教程
- • Java Agent入門實戰（一）-Instrumentation介紹與使用
- • Java Agent入門實戰（三）-JVM Attach原理與使用
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. hand on machine learning with
- 2. [GPU] Machine Learning on C++
- 3. Chapter 9 On-policy Prediction with Approximation
- 4. 【機器學習中的公平性】Fairness in Machine Learning
- 5. Tackling Climate Change with Machine Learning
- 6. Machine and Deep Learning with Python
- 7. An Introduction to Machine Learning with Python
- 8. Fixed error when submitting assignments in Machine Learning on Coursera
- 9. 論文解讀：Multimodal Machine Translation with Embedding Prediction
- 10. Classification and inference with machine learning