Mitigating Bias in Machine Learning
暫譯: 減少機器學習中的偏見

Berry, Carlotta A., Marshall, Brandeis Hill

  • 出版商: McGraw-Hill Education
  • 出版日期: 2024-10-02
  • 定價: $1,800
  • 售價: 9.0$1,620
  • 語言: 英文
  • 頁數: 304
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1264922442
  • ISBN-13: 9781264922444
  • 相關分類: Machine Learning
  • 立即出貨 (庫存=1)

商品描述

This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.

Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.

Mitigating Bias in Machine Learning addresses:

 

  • Ethical and Societal Implications of Machine Learning
  • Social Media and Health Information Dissemination
  • Comparative Case Study of Fairness Toolkits
  • Bias Mitigation in Hate Speech Detection
  • Unintended Systematic Biases in Natural Language Processing
  • Combating Bias in Large Language Models
  • Recognizing Bias in Medical Machine Learning and AI Models
  • Machine Learning Bias in Healthcare
  • Achieving Systemic Equity in Socioecological Systems
  • Community Engagement for Machine Learning

 

商品描述(中文翻譯)

這本實用指南逐步展示了如何使用機器學習來做出不基於多種人類因素(包括種族和性別)歧視的可行決策。作者探討了當前領域中出現的多種偏見,並提供了可以在各種技術、應用和行業中立即部署的緩解策略。

《減輕機器學習中的偏見》由工程和計算專家編輯,包含來自不同人工智慧領域的知名學者和專業人士的貢獻。每一章都針對不同主題,並在整本書中展示了真實案例研究,突顯了歧視性機器學習實踐,並清楚顯示了如何減少這些實踐。

《減輕機器學習中的偏見》涵蓋了以下主題:

- 機器學習的倫理和社會影響
- 社交媒體與健康資訊的傳播
- 公平工具包的比較案例研究
- 仇恨言論檢測中的偏見緩解
- 自然語言處理中的意外系統性偏見
- 對大型語言模型中的偏見進行打擊
- 辨識醫療機器學習和人工智慧模型中的偏見
- 醫療保健中的機器學習偏見
- 在社會生態系統中實現系統性公平
- 機器學習的社區參與