Commentary: What if the HR department's AI prefers resumes written by AI?

If AI is biased towards AI, it adds a fresh dimension of jeopardy in the dismal hiring arms race, says the Financial Times’ Pilita Clark.

A company compared how an AI agent assessed job applications compared with humans.

This audio is generated by an AI tool.

LONDON: Earlier this year, in the depths of summer, the Addleshaw Goddard law firm found itself in a slight pickle. In what was becoming a familiar pattern, an advert it had placed for an IT job in its human resources department had drawn a slew of applications.

A year ago, only a couple of dozen people might have sent in a resume for a position like this one. But AI has made the job of applying for a job much faster and easier, and this time 178 applications arrived.

The problem was, the longer it took to sift them, the higher the chance that good candidates would find work elsewhere. Like a lot of organisations, Addleshaw has been encouraging its managers to test ways that AI can safely improve its processes, so the firm’s HR department decided to do an experiment.

It would see how well an AI agent could assess the applications compared with humans.

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AI AND HUMANS PREFERRED DIFFERENT APPLICATIONS

An agent was duly set up, using the firm’s internal Microsoft Copilot system, and told about the role that needed to be filled, as well as the experience applicants would need with certain HR systems and programming languages, and with AI itself.

Humans at the firm sorted the applicants into a shorter pile of about a dozen and asked the agent to score them.

The agent whipped through this task and presented a list of what it deemed the top contenders. The humans did the same thing.

Alas, the two lists did not match. The agent was impressed by glossy applications from candidates who claimed to have achieved exceptional IT feats in record time at a previous employer, or worked on big projects despite scant levels of experience. In short, they were too good to be true – and smacked of having been written with the help of AI.

Katherine Rathbone, Addleshaw Goddard’s head of HR, told me her team had theories about why this might have happened.

For one thing, AI tools can make it a lot easier for candidates to tailor a CV to match a job description. But that was not all. “One possible explanation is that, because AI capability was itself one of the criteria, the agent may have interpreted signs of AI use in producing the CV as evidence of AI capability,” she said.

USING THE SAME AI AS THE RECRUITER?

There is another, more disturbing possibility. The AI agent may have actively preferred CVs purely because AI was involved in writing them.

If so, it would confirm the findings of an eye-catching working paper that has analysed the behaviour of AI hiring tools.

The researchers simulated what they said were realistic hiring processes for 24 jobs ranging from teachers and consultants to chefs and engineers. They discovered that candidates using the same AI large language model as the recruiter were between 23 per cent and 60 per cent more likely to be shortlisted than those who relied on CVs produced by a hapless human.

This human handicap was highest for business roles such as sales and accountancy and lowest for fields such as farming and automobiles.

The paper is still being revised for publication in a peer-reviewed journal, meaning some of its findings may change. But one of its authors, Jiannan Xu, told me that updated analyses continue to provide evidence that AI models “exhibit self-preferencing behaviour”.

DISTURBING NEWS IN THE HIRING ARMS RACE

That is disturbing news at a time when AI is already driving a dismal hiring arms race. On one side are job hopefuls who, convinced they are battling a wall of soulless recruitment tools, are fighting back with an explosion of AI applications that are swamping recruiters.

If AI is biased towards AI, it adds a fresh dimension of jeopardy. Imagine being the candidate using the wrong type of AI, or no AI at all.

Fortunately, Xu’s paper suggests AI bias can be more than halved with two relatively simple strategies: telling models to ignore the origin of resumes and focus on their content, and using a number of assessment models to dilute the bias of any single tool.

Meanwhile at Addleshaw Goddard, I was pleased to learn that the successful candidate for the IT job was chosen by humans, and was not the one the agent ranked highest.

And as for using AI for recruitment, it is still a work in progress. The tools clearly have potential if used correctly, says Rathbone. “But human judgment remains important.”

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《金融时报》的皮利塔·克拉克表示，如果人工智能对人工智能存在偏见，那么在令人沮丧的招聘竞赛中，这将增添新的危险因素。

一家公司对比了人工智能代理和人类评估求职申请的效果。

这段音频由人工智能工具生成。

伦敦：今年早些时候，正值盛夏，Addleshaw Goddard律师事务所遇到了一点小麻烦。不出所料，该事务所在其人力资源部门发布的IT职位招聘广告收到了大量申请。

一年前，像这样的职位可能只有二三十人会投递简历。但人工智能让求职过程变得更加快捷方便，这次我们收到了178份申请。

问题在于，筛选简历的时间越长，优秀候选人找到其他工作的可能性就越大。和许多企业一样，Addleshaw 一直鼓励其管理人员测试人工智能如何安全地改进流程，因此公司的人力资源部门决定进行一项实验。

它将检验人工智能代理评估应用程序的能力与人类相比如何。

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人工智能和人类偏好不同的应用场景

公司使用内部的 Microsoft Copilot 系统，妥善地设置了一个代理，并告知其需要填补的职位，以及申请人需要具备的某些人力资源系统和编程语言方面的经验，以及人工智能本身方面的经验。

公司里的工作人员将申请人分成大约十几人的小堆，然后请经纪人给他们打分。

智能体迅速完成了这项任务，并列出了它认为的最佳候选名单。人类也做了同样的事情。

可惜，两份名单并不相符。经纪人被那些简历写得花里胡哨的求职者所吸引，他们声称自己曾在前雇主那里以创纪录的速度取得了非凡的IT成就，或者经验不足却参与过大型项目。简而言之，这些简历好得令人难以置信——而且明显是人工智能生成的。

Addleshaw Goddard 的人力资源主管 Katherine Rathbone 告诉我，她的团队对这件事发生的原因有一些猜测。

首先，人工智能工具可以帮助求职者更轻松地根据职位描述定制简历。但这并非全部。“一种可能的解释是，由于人工智能能力本身就是评判标准之一，因此，系统可能将简历生成过程中人工智能的使用痕迹解读为人工智能能力的证据，”她说道。

使用与招聘人员相同的AI？

还有一种更令人不安的可能性。人工智能代理可能仅仅因为简历是由人工智能参与撰写的，就主动偏爱这些简历。

如果属实，这将证实一篇引人注目的工作论文的结论，该论文分析了人工智能招聘工具的行为。

研究人员模拟了他们所说的24种不同职业的真实招聘流程，这些职业涵盖教师、顾问、厨师和工程师等。他们发现，使用与招聘人员相同的AI大型语言模型的求职者，比那些依赖由不称职的人类撰写的简历的求职者，入围的可能性高出23%到60%。

这种人类缺陷在销售和会计等商业岗位中最为严重，而在农业和汽车等领域中最低。

该论文仍在修改中，尚未在同行评审期刊上发表，这意味着部分研究结果可能会有所改变。但其中一位作者徐建南告诉我，更新后的分析仍然提供证据表明，人工智能模型“表现出自我偏好行为”。

招聘竞赛中令人不安的消息

在人工智能已经引发一场令人沮丧的招聘竞赛之际，这无疑是个令人不安的消息。一方面，求职者们深信自己正与一堵冷冰冰的招聘工具之墙作战，于是他们大量涌入人工智能应用程序进行反击，这些应用程序正使招聘人员应接不暇。

如果人工智能本身就带有偏见，那就会带来新的风险。试想一下，如果你是候选人，却使用了错误的人工智能，或者根本没有使用人工智能，那会是什么情况？

幸运的是，徐的论文表明，通过两种相对简单的策略，可以将人工智能的偏见减少一半以上：让模型忽略简历的来源，专注于其内容；以及使用多个评估模型来稀释任何单一工具的偏见。

与此同时，在 Addleshaw Goddard，我很高兴地得知，IT 职位的最终人选是由人工选出的，而不是代理人排名最高的人选。

至于将人工智能应用于招聘，目前仍处于发展阶段。拉思伯恩表示，如果使用得当，这些工具显然具有潜力。“但人的判断仍然至关重要。”

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