September 2025
Intermediate to advanced
476 pages
7h 51m
Japanese
book
高リスク分野のための機械学習 ―責任あるAI構築のための実践アプローチ
by Patrick Hall, James Curtis, Parul Pandey, 高江洲 勲, 伊東 道明, 園田 道夫, 北條 孝佳, 石川 太一
Content preview from 高リスク分野のための機械学習 ―責任あるAI構築のための実践アプローチ
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2章解釈可能性と説明可能性
科学者は、観測されたパターンをより深く理解するために、何世紀にもわたってデータにモデルを当てはめてきた。長期間にわたる実践の中で、説明可能な機械学習モデルや事後説明手法が、漸進的ながらも重要な進歩をもたらしてきた。従来の線形モデルに比べ、機械学習モデルは非線形で微弱な信号や相互作用する信号を容易に学習できるため、これらの技術を用いることで、データ中の複雑な信号を人間がより簡単に理解できるようになった。
本章では、主要な説明可能モデリングや事後説明手法に取り組む前に、まず解釈や説明のための重要な概念について深く掘り下げる。これらの概念は、説明可能なモデルと事後説明手法を併用することで、多くの場合克服することができる。次に、説明可能なモデルや事後説明手法の応用例として、モデルの文書化や誤った決定に対して採りうる救済措置など、AIシステムの説明責任を高めるための実践例を説明する。さらに本章では、説明可能で詳細に文書化されたモデルが説明不可能な決定を下し、全国的なAIインシデントを引き起こした英国の「Aレベル試験スキャンダル」の事例を採り上げる。なお、説明可能なモデルと事後説明手法に関する議論は、6章と7章に続き、これらのトピックに関連する2つの詳細なコード例の探求へと展開していく。
NIST AI RMFクロスウォーク
| 該当する箇所 | NIST AI RMFのサブカテゴリ |
|---|---|
| 「2.1 解釈可能性と説明可能性のための重要な考え方」 | GOVERN 1.1、GOVERN 1.2、GOVERN 1.4、GOVERN 1.5、GOVERN 3.2、GOVERN 4.1、GOVERN 5.1、GOVERN 6.1、MAP 2.3、MAP 3.1、MAP 4、MEASURE ... |
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.