商品描述
How tech companies, journalists, and policymakers can prevent AI decision-making from going wrong. Our lives are increasingly governed by automated systems influencing everything from medical care to policing to employment opportunities, but researchers and investigative journalists have proven that AI systems regularly get things wrong.
Auditing AI is a first-of-its-kind exploration of why and how to audit artificial intelligence systems. It offers a simple roadmap for using AI audits to make product and policy changes that benefit companies and the public alike. The book aims to convince readers that AI systems should be subject to robust audits to protect all of us from the dangers of these systems. Readers will come away with an understanding of what an AI audit is, why AI audits are important, key components of an audit that follows best practices, how to interpret an audit, and the available choices to act on an audit's results.
The book is organized around canonical examples: from AI-powered drones mistakenly targeting civilians in conflict areas to false arrests triggered by facial recognition systems that misidentified people with dark skin tones to HR hiring software that prefers men. It explains these definitive cases of AI decision-making gone wrong and then highlights specific audits that have led to concrete changes in government policy and corporate practice.
The Marquand House Collective: Marc Aidinoff, Lena Armstrong, Esha Bhandari, Ellery Roberts Biddle, Motahhare Eslami, Karrie Karahalios, Nate Matias, Danaé Metaxa, Alondra Nelson, Christian Sandvig, and Kristen Vaccaro.
商品描述(中文翻譯)
科技公司、記者和政策制定者如何防止人工智慧決策出錯。
我們的生活越來越受到自動化系統的影響，這些系統影響著從醫療護理到執法再到就業機會的方方面面，但研究人員和調查記者已證明，人工智慧系統經常出錯。
審計人工智慧 是首次探索為何以及如何審計人工智慧系統的著作。它提供了一個簡單的路線圖，說明如何利用人工智慧審計來進行產品和政策的變更，以造福公司和公眾。這本書旨在說服讀者，人工智慧系統應該接受嚴格的審計，以保護我們所有人免受這些系統的危險。讀者將了解什麼是人工智慧審計、為什麼人工智慧審計很重要、遵循最佳實踐的審計的關鍵組成部分、如何解讀審計結果，以及根據審計結果採取行動的可用選擇。
本書圍繞經典案例組織：從人工智慧驅動的無人機錯誤鎖定衝突區域的平民，到因面部識別系統錯誤識別深色皮膚人群而引發的錯誤逮捕，再到偏好男性的HR招聘軟體。它解釋了這些人工智慧決策出錯的明確案例，然後強調了導致政府政策和企業實踐具體變化的特定審計。
Marquand House Collective: Marc Aidinoff, Lena Armstrong, Esha Bhandari, Ellery Roberts Biddle, Motahhare Eslami, Karrie Karahalios, Nate Matias, Danaé Metaxa, Alondra Nelson, Christian Sandvig, 和 Kristen Vaccaro。
作者簡介
The Marquand House Collective comprises eleven experts in AI auditing spanning computing, law, policy, social science, and journalism. Members coined the term "algorithm audit" in 2014. The full group convened in 2024 at Marquand House in Princeton, New Jersey.
作者簡介(中文翻譯)
Marquand House Collective 由十一位專精於人工智慧審計的專家組成，涵蓋計算機、法律、政策、社會科學和新聞學等領域。成員們於2014年創造了「演算法審計」這個術語。整個團體於2024年在新澤西州普林斯頓的 Marquand House 召開會議。
