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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移动应用程序的道德、公平和隐私
虽然机器学习和人工智能的最新进展使伦理和公平的概念成为人们关注的焦点，但重要
的是要注意不平等和不公平一直是计算机系统关注的话题。在我的职业生涯中，我见过
很多例子，其中一个系统是为一种场景设计的，而没有考虑公平和偏见的整体影响。
考虑这个例子：你的公司有一个客户数据库，并希望针对特定的邮政编码地区开展营销
活动以吸引更多客户，在那里确定有一个增长机会。为此，该公司将向该邮政编码地区
中有联系但尚未购买任何东西的人发送折扣优惠券。你可以像这样编写 SQL 来识别这些
潜在客户：
SELECT * from Customers WHERE ZIP=target_zip AND PURCHASES=0
这似乎是完全合理的代码。但请考虑该邮政编码地区的人口统计数据。如果住在那里的
大多数人属于特定种族或年龄怎么办？你可能没有逐渐地增加你的客户群，而是过度关
注了一个群体，或者更糟的是，通过向一个种族的人提供折扣而不是另一个种族的人，
从而歧视了另一个群体。随着时间的推移，像这样的持续定位可能会导致客户群与社会
人口统计数据发生偏差，最终将使你的公司主要服务于一个细分市场。在这种情况下，
变量“邮政编码”是显式的，但具有不那么显式的代理变量的系统仍然可以在没有仔细
监控的情况下发展为有偏差的系统。
基于 AI 的系统的承诺是，你将能够更快地交付更强大的应用程序……但如果你这样做
的代价是没有减少系统中的偏见，那么你可能会通过使用 AI 加速了不平等。
在可能的情况下，理解和消除这一点的过程是一个巨大的领域 ...
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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.