January 2023
Intermediate to advanced
288 pages
6h 6m
Chinese
Content preview from 移动端AI与ML应用开发：基于iOS和Android
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262
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第
15
章
15.1
负责任的人工智能的道德、公平和隐私
构建一个将负责任作为 DNA 一部分的 AI 系统意味着可以在 ML 工作流的每个步骤中纳
入负责任的 AI 实践。虽然有很多模式，但我将遵循非常通用的模式并执行以下步骤：
1.
定义问题：谁使用你的机器学习系统
2.
构建和准备数据
3.
构建和训练模型
4.
评估你的模型
5.
部署和监控模型的使用
让我们看看在你处理这些步骤时可以使用的一些工具。
15.1.1
负责任地定义你的问题
当你创建应用程序来解决问题时，最好考虑应用程序存在可能出现的问题。你可能会构
建一些像鸟鸣检测器一样的东西，你可以在其中根据鸟类发出的声音对它们进行分类。
但这会如何影响你的用户呢？如果你的数据仅限于在特定地区生存的鸟类，并且该地区主
要由单一鸟群居住，该怎么办？你会在不知不觉中编写一个仅适用于该鸟群的应用程序。
那是你要的吗？使用这样的应用程序，也会出现可访问性问题。如果你的想法是你听到一
只鸟儿在唱歌，并且你想识别它……你假设此人可以听到鸟儿的声音，所以你没有包括听
力下降或没有听力的人。现在虽然这是一个非常简单的例子，但这个概念可以扩展到深
刻影响某人生活的应用程序或服务。如果你的拼车应用避开了某些社区，把某些人排除在
外怎么办？如果你的应用程序对医疗保健有用，例如帮助人们管理他们的药物，但它不适
用于特定人群怎么办？很容易想象你可能会对应用程序造成哪些伤害，即使这些后果是无
意的。因此，注意所有潜在用户并拥有帮助指导你完成此操作的工具非常重要。
无法预测所有可能无意中引入偏见的情况，因此，考虑到这一点， ...
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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.