来源：《新科学家》
原文刊登日期：2022年4月1日
When people use gender-neutral words like “people” and “humanity” they tend to be thinking of men rather than women, in reflection of sexism present in many societies, according to an analysis of billions of words published online. The researchers behind the work warn that this sexist bias is being passed on to artificial intelligence models that have been trained on the same text.
一项对网上发表的数十亿个单词的分析显示，当人们使用“people”和“humanity”等中性词汇时，他们想到的往往是男性而不是女性，这反映了许多社会存在的性别歧视。这项工作背后的研究人员警告说，这种性别偏见正被传递给根据相同文本训练的人工智能模型。
April Bailey at New York University and colleagues used a statistical algorithm to analyse a collection of 630 billion words contained within 2.96 billion web pages gathered in 2017, including informal text from blogs and discussion forums as well as more formal text written by the media, corporations and governments, mostly in English. They used an approach called word embedding which derives the intended meaning of a word by the frequency it occurs in context with other words.
纽约大学的贝利及其同事使用一种统计算法分析了2017年收集的29.6亿个网页中包含的6300亿个单词，包括博客和论坛的非正式文本，以及媒体、企业和政府撰写的更多正式文本，主要是英语。他们使用了一种名为“词嵌入”的方法，通过一个词与其他词在上下文中出现的频率来得出该词的本意。
They found that words like “person”, “people” and “humanity” are used in contexts that better match the context of words like “men”, “he” and “male” than those of words like “women”, “she” and “her”. The team says that because these gender-inclusive words were used more similarly to those that refer to men, people may see them as more male in their conceptual meaning – a reflection of male-dominated society.
他们发现，像“person”、“people”和“humanity”这样的词的上下文与“men”、“he”和“male”这样的词的上下文更匹配，相比于“women”、“she”和“her”这样的词。研究小组表示，由于这些性别包容性词汇的使用与那些指男性的词汇更相似，人们可能会认为人在概念上更男性化——这反映了男性主导的社会。
Bailey says that finding evidence of sexist bias in English is unsurprising, as previous studies have shown that words like “scientist” and “engineer” are also considered to be more closely linked with words like “man” and “male” than with “woman” and “female”. But she says it should be concerning because the same collection of texts scoured by this research is used to train a range of AI tools that will inherit this bias, from language translation websites to conversational bots.
贝利说，在英语中发现性别歧视的证据并不令人惊讶，因为此前的研究已经表明，像“科学家”和“工程师”这样的词也被认为与“男人”和“男性”的联系更紧密，而不是与“女人”和“女性”的联系更紧密。但她说，这应该引起关注，因为这项研究收集的同一批文本被用来训练一系列将继承这种偏见的人工智能工具，从语言翻译网站到会话机器人。
“It learns from us, and then we learn from it,” says Bailey. “And we’re kind of in this reciprocal loop, where we’re reflecting it back and forth. It’s concerning because it suggests that if I were to magically get rid of everyone’s own individual cognitive bias to think of a person as a man more than a woman, we would still have this bias in our society because it’s embedded in AI tools.”
“AI向我们学习，然后我们向AI学习，”贝利说。“我们处于一种相互循环中，在这种循环中，我们来回地反映它。这是令人担忧的，因为它表明，如果我神奇地使每个人摆脱个人认知偏见，即人更多的时候指的是男人而不是女人，但我们在社会中仍然会有这种偏见，因为它已经嵌入到人工智能工具中。”