内容来源：https://www.sciencenews.org/article/bias-online-images-women-ai-hiring

内容总结：

【新华社专电】最新研究表明，人工智能技术正通过有偏差的网络数据强化对女性的年龄与职业歧视。国际学术期刊《自然》10月8日刊发的研究报告显示，当被要求生成女性姓名（如艾莉森·贝克、玛丽亚·加西亚）和男性姓名（如马修·欧文斯、乔·阿尔瓦雷斯）的求职简历时，ChatGPT生成的女性候选人平均比男性候选人年轻1.6岁，并据此将女性求职者评定为资历不足，形成自我强化的性别年龄偏见闭环。

值得关注的是，这种对职场中"年轻女性"与"年长男性"的偏好与现实情况严重脱节。美国人口普查数据显示，该国职场男女年龄结构基本持平。更令人担忧的是，即使在销售服务等女性平均年龄普遍高于男性的行业，该人工智能仍持续表现出对女性的年龄歧视。

研究团队通过分析近140万份网络图像视频、开展文本分析及随机对照实验证实，存在偏差的网络数据正在扭曲人工智能的输出结果。斯坦福大学计算社会科学家道格拉斯·吉尔伯特指出，这种现象或可解释女性职场"玻璃天花板"难以打破的深层原因："许多企业虽积极招聘年轻女性，却未给予同等晋升机会。"

研究发现，在分析网络图像时，编程人员普遍将图像中的女性判定为较男性更年轻，这种认知偏差在医生、首席执行官等权威职业中尤为显著。文本分析进一步证实，秘书、实习生等初级职位常与年轻女性关联，而董事会主席、研究主任等高级职位则多与年长男性绑定。

专家警告，随着人工智能应用普及，此类偏见可能持续加剧。现有技术方案往往单独应对某类歧视，却忽视了性别与年龄、种族与阶级等交叉性歧视的复合影响。正如研究者强调："真正的歧视源于多重不平等的交织作用。"这项研究为科技伦理治理敲响警钟，提示应建立更全面的算法审计体系，从数据源头阻断偏见的代际传递。

中文翻译：

带有偏见性的网络图片会训练人工智能将女性视为更年轻、缺乏经验的群体

一项研究揭示了网络上男女年龄差异如何塑造人工智能对职场人士的认知
作者：苏嘉塔·古普塔

研究人员在10月8日《自然》杂志发表的研究报告中指出，当被要求为艾莉森·贝克、玛丽亚·加西亚等女性姓名者与马修·欧文斯、乔·阿尔瓦雷斯等男性姓名者生成简历时，ChatGPT平均会将女性求职者设定得比男性年轻1.6岁。这种人工智能随后在自我实现的循环中，将女性应聘者评定为资质低于男性，显现出年龄与性别双重偏见。

然而该人工智能模型对职场中年轻女性和年长男性的偏好并不符合现实。根据美国人口普查数据，美国职场中男女员工年龄基本相当。更值得注意的是，即使在销售服务等女性实际年龄通常高于男性的行业，该聊天机器人仍表现出相同的年龄性别偏见。

未参与该研究的宾夕法尼亚大学计算机科学家达纳埃·梅塔克萨指出，职场中对年长女性的歧视众所周知但难以量化验证。这项关于普遍存在的“性别化年龄歧视”的发现具有现实意义：“女性看到自己被刻画成三四十岁就过了职业黄金期的形象，这种既成印象具有显著危害性。”

研究团队通过分析近140万条网络图片视频、文本分析及随机对照实验等多种方法，证实失实的信息输入如何扭曲人工智能输出——本案中表现为对特定人口群体简历的偏好。

研究合著者、计算社会科学家道格拉斯·吉尔伯特认为，这些发现可解释女性职场晋升障碍持续存在的原因。过去十年众多机构试图增加女性招聘，但研究显示企业最高管理层仍由男性主导。斯坦福大学的吉尔伯特表示：“追求多元化的组织...招聘年轻女性却不予晋升。”

在研究第一阶段，吉尔伯特团队让6000多名编码员判断谷歌、维基百科等网络平台各行业人物图片的年龄，并对YouTube视频中的工作者进行年龄评估。编码员持续将图像中的女性判定得比男性更年轻，这种偏见在医生、首席执行官等权威职业中最为显著，暗示社会认知中将年长男性而非年长女性与权威性关联。

团队还运用九种语言模型分析网络文本，以排除修图滤镜或化妆品等视觉干扰因素。文本分析显示，秘书、实习生等普通职位与年轻女性产生关联，而董事会主席、研究主任等高级职位则与年长男性相绑定。

随后团队对450余人开展实验，验证网络失真信息是否影响人类认知。实验组通过谷歌图片搜索数十种职业形象，将图片上传至研究数据库并进行性别标注与年龄评估，对照组则上传随机图片并在无图像辅助下评估各行业平均年龄。

研究发现图片上传确实影响认知：上传数学家、平面设计师、美术教师等女性从业者图片的参与者，对该职业平均年龄的评估较对照组年轻两岁；相反，上传男性从业者图片的参与者则会将同行平均年龄评估偏高逾半年。

团队进一步证实，基于海量网络图像视频文本训练的人工智能模型正在继承并放大这种年龄性别偏见。研究人员令ChatGPT使用16个男女姓名为54种职业生成简历，每组近1.73万份，随后让人工智能对简历进行百分制评分。该系统持续为女性生成更年轻、资历更浅的简历，并给予较低评分。

吉尔伯特指出这种社会偏见危害全民：人工智能对年轻男性简历的评分也低于年轻女性。

意大利菲耶索莱欧洲大学学院社会学家安娜·马卡诺维奇在配套评论文章中警示，随着人工智能普及，此类偏见可能加剧。

吉尔伯特表示，谷歌及ChatGPT母公司OpenAI等企业通常逐项解决种族主义或性别歧视等偏见，但这种单一处理方式忽略了性别与年龄、种族与阶级等交叉偏见。他以增加网络黑人形象为例说明：若忽视与种族多样性缺失相关的交叉偏见，网络生态可能充斥白人精英与黑人贫民的刻板印象，“真正的歧视源于多重不平等的交织作用。”

英文来源：

Biased online images train AI bots to see women as younger, less experienced
A study tracks how online age gaps between men and women shape AI’s perception of employees
By Sujata Gupta
When asked to generate resumes for people with female names, such as Allison Baker or Maria Garcia, and people with male names, such as Matthew Owens or Joe Alvarez, ChatGPT made female candidates 1.6 years younger, on average, than male candidates, researchers report October 8 in Nature. In a self-fulfilling loop, the bot then ranked female applicants as less qualified than male applicants, showing age and gender bias.
But the artificial intelligence model’s preference for younger women and older men in the workforce does not reflect reality. Male and female employees in the United States are roughly the same age, according to U.S. Census data. What’s more, the chatbot’s age-gender bias appeared even in industries where women do tend to skew older than men, such as those related to sales and service.
Discrimination against older women in the workforce is well known, but it has been hard to prove quantitatively, says computer scientist Danaé Metaxa of the University of Pennsylvania, who was not involved with the study. This finding of pervasive “gendered ageism” has real world implications. “It’s a notable and harmful thing for women to see themselves portrayed … as if their lifespan has a story arc that drops off in their 30s or 40s,” they say.
Using several approaches, including an analysis of almost 1.4 million online images and videos, text analysis and a randomized controlled experiment, the team showed how skewed information inputs distorts AI outputs — in this case a preference for resumes belonging to certain demographic groups.
These findings could explain the persistence of the glass ceiling for women, says study coauthor and computational social scientist Douglas Guilbeault. Many organizations have sought to hire more women over the past decade, but men continue to occupy companies’ highest ranks, research shows. “Organizations that are trying to be diverse … hire young women and they don’t promote them,” says Guilbeault, of Stanford University.
In the study, Guilbeault and colleagues first had more than 6,000 coders judge the age of individuals in online images, such as those found on Google and Wikipedia, across various occupations. The researchers also had coders rate workers depicted in YouTube videos as young or old. The coders consistently rated women in images and videos as younger than men. That bias was strongest in prestigious occupations, such as doctors and chief executive officers, suggesting that people perceive older men, but not older women, as authoritative.
The team also analyzed online text using nine language models to rule out the possibility that women appear younger online due to visual factors such as image filters or cosmetics. That textual analysis showed that less prestigious job categories, such as secretary or intern, linked with younger females and more prestigious job categories, such as chairman of the board or director of research, linked with older males.
Next, the team ran an experiment with over 450 people to see if distortions online influence people’s beliefs. Participants in the experimental condition searched for images related to several dozen occupations on Google Images. They then uploaded images to the researchers’ database, labeled them as male or female and estimated the age of the person depicted. Participants in the control condition uploaded random pictures. They also estimated the average age of employees in various occupations, but without images.
Uploading pictures did influence beliefs, the team found. Participants who uploaded pictures of female employees, such as mathematicians, graphic designers or art teachers, estimated the average age of others in the same occupation as two years younger than participants in the control condition. Conversely, participants who uploaded the picture of male employees in a given occupation estimated the age of others in the same occupation as more than half a year older.
AI models trained on the massive online trove of images, videos and text are inheriting and exacerbating age and gender bias, the team then demonstrated. The researchers first prompted ChatGPT to generate resumes for 54 occupations using 16 female and 16 male names, resulting in almost 17,300 resumes per gender group. They then asked ChatGPT to rank each resume on a score from 1 to 100. The bot consistently generated resumes for women that were younger and less experienced than those for men. It then gave those resumes lower scores.
These societal biases hurt everyone, Guilbeault says. The AIs also scored resumes from young men lower than resumes from young women.
In an accompanying perspective article, sociologist Ana Macanovic of European University Institute in Fiesole, Italy, cautions that as more people use AI, such biases are poised to intensify.
Companies like Google and OpenAI, which owns ChatGPT, typically try to tackle one bias at a time, such as racism or sexism, Guilbeault says. But that narrow approach overlooks overlapping biases, such as gender and age or race and class. Consider, for instance, efforts to increase the representation of Black people online. Absent attention to biases that intersect with the shortage of racially diverse images, the online ecosystem may become flooded with depictions of rich white people and poor Black people, he says. “Real discrimination comes from the combination of inequalities.”