Fairness and Machine Learning: Limitations and Opportunities

Barocas, Solon, Hardt, Moritz, Narayanan, Arvind

Fairness and Machine Learning: Limitations and Opportunities
  • 出版商: MIT
  • 出版日期: 2023-12-19
  • 售價: $2,340
  • 貴賓價: 9.5 折 $2,223
  • 語言: 英文
  • 頁數: 340
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 0262048612
  • ISBN-13: 9780262048613
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

商品描述

An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning.

Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making. It surveys the risks in many applications of machine learning and provides a review of an emerging set of proposed solutions, showing how even well-intentioned applications may give rise to objectionable results. It covers the statistical and causal measures used to evaluate the fairness of machine learning models as well as the procedural and substantive aspects of decision-making that are core to debates about fairness, including a review of legal and philosophical perspectives on discrimination. This incisive textbook prepares students of machine learning to do quantitative work on fairness while reflecting critically on its foundations and its practical utility.

- Introduces the technical and normative foundations of fairness in automated decision-making
- Covers the formal and computational methods for characterizing and addressing problems
- Provides a critical assessment of their intellectual foundations and practical utility
- Features rich pedagogy and extensive instructor resources

商品描述(中文翻譯)

公平性與機器學習的知識基礎及實用性介紹。

《公平性與機器學習》為高年級本科生及研究生介紹這一新興領域的知識基礎，並從多元學科的視角出發，識別自動化決策的機會與風險。書中調查了機器學習在多種應用中的風險，並回顧了一系列新興的解決方案，顯示即使是出於良好意圖的應用也可能產生令人反感的結果。內容涵蓋了用於評估機器學習模型公平性的統計和因果測量，以及與公平性辯論核心相關的決策程序和實質方面，包括對歧視的法律和哲學觀點的回顧。這本深刻的教科書為機器學習的學生準備了進行公平性定量研究的基礎，同時對其基礎和實用性進行批判性反思。

- 介紹自動化決策中公平性的技術和規範基礎
- 涵蓋表徵和解決問題的正式和計算方法
- 提供對其知識基礎和實用性的批判性評估
- 具備豐富的教學法和廣泛的教師資源

作者簡介

Solon Barocas is a Principal Researcher in the New York City lab of Microsoft Research, where he is a member of the Fairness, Accountability, Transparency, and Ethics in AI (FATE) research group. He is an Adjunct Assistant Professor in the Department of Information Science at Cornell University and Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University.

Moritz Hardt is Director of Social Foundations of Computation at the Max Planck Institute for Intelligent Systems and coauthor of Patterns, Predictions, and Actions: Foundations of Machine Learning.

Arvind Narayanan is Professor of Computer Science at Princeton University and director of the Center for Information Technology Policy. His work was among the first to show how machine learning reflects cultural stereotypes, and he led the Princeton Web Transparency and Accountability Project to uncover how companies collect and use our personal information.

作者簡介(中文翻譯)

Solon Baracas 是微軟研究院紐約市實驗室的首席研究員，並且是公平性、問責性、透明性與人工智慧倫理（FATE）研究小組的成員。他是康奈爾大學資訊科學系的兼任助理教授，以及哈佛大學伯克曼克萊因網路與社會中心的教職成員。

Moritz Hardt 是馬克斯·普朗克智能系統研究所計算社會基礎的主任，並且是《模式、預測與行動：機器學習的基礎》的共同作者。

Arvind Narayanan 是普林斯頓大學的計算機科學教授，並且是資訊技術政策中心的主任。他的研究是最早顯示機器學習如何反映文化刻板印象的工作之一，他還主導了普林斯頓網路透明度與問責專案，以揭露公司如何收集和使用我們的個人資訊。

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