计算机将确定下一个薪水的大小

On March 30, a court ruling against Goldman Sachs made Wall Street compensation consultants sit bolt upright.

In the 49-page document, U.S. District Judge Analisa Torres wrote that a group of women could proceed with a class-action lawsuit that accused the investment bank of gender discrimination regarding performance reviews, promotions — and pay. The ruling stated that the women had offered “significant proof of discriminatory disparate treatment” at Goldman.

这种情况本身并不是什么新鲜事：长期延迟的诉讼实际上是八年前的初步，而且它只是过去几十年来投资银行的许多类似索赔之一。但是，关于托雷斯裁决的事情是时机。在过去三年中，几个国家 - 包括纽约，新泽西州和加利福尼亚 - 已经通过了严格的新薪酬股权法，要求公司支付属于某些所谓受保护的课程的员工，例如种族或性别，同样作为具有相同职责的其他员工。

The laws go further than federal equal-pay statutes in that they essentially lower the burden of proof for employees to make a credible claim. Workers simply need to show that positions are sufficiently similar and the pay isn’t. They don’t even need to have the same job title, according to Adam Zoia, a veteran executive recruiter and founder and chair of search firm Glocap Search who recently co-founded CompIQ, a software company dedicated to bringing pay into the artificial intelligence age.

“Now it’s not good enough to say we are doing the best we can,” says Zoia. “The legislation is creating liability if you don’t offer equal pay for equal work.”

Salary.com的高级经理Sarah Reynolds同意。“没有人会说他们以前从未看过他们的数据，所以他们不能持有责任，所以他们说。”

And even if the reasons for pay disparities aren’t nefarious — say one employee simply negotiated harder for a higher salary — companies will still be on the hook for them.

“赔偿行业在手动分析的不精确数据上运作，”Zoia说。“他们不会为每个员工经历它;太贵了。所以有很多不公平的工资，不是恶意“，但由于信息不完整，他说。但新的法律不会对这些情况进行异常。“随着更多的诉讼发生，这将成为一个问题。”

尽管如此，薪水仍然是公司的棘手问题，这些公司必须考虑到无数变量，如工作职责，资历，地理位置，市场竞争，甚至办公室政治。那么如何留在法律的右侧？

Enter the algos.

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弄清楚要支付员工的东西is a $12 billion business, according to Zoia.

“COMP中的圣杯正在将苹果与苹果进行比较。但职责周围有细微差别。你正在比较标题层面的人，没有保证他们正在做同样的事情，“他解释道。“这是一个众所周知的问题。对于人们支付最高薪水，你经常不知道为什么。他们是否得到额外的钱，因为他们已经存在了十年？他们是一个摇滚明星吗？他们有额外的责任吗？“

That’s where AI and machine learning techniques come in. (Machine learning is the “how” and AI is the “what.” That is, AI is powered by machine learning.) Long heralded for their promise in portfolio management, AI and machine learning are now being applied to the back office. By comparing data points for salaries across numerous companies, locations, job titles, responsibilities, and so on, computers can triangulate data points to arrive at a number.

以下是Compiq的软件工作原理：客户公司上传其员工的部门，标题，位置，绩效审查和赔偿，并在一个单独的过程中，员工指出其职责，这些责任由他们的经理确认。然后将数据加密在传输中，并且在此信息中运行机器学习模型。客户端查看每个员工特定的自定义补偿基准信息。

The software is meant to automate processes Zoia describes as “manual, expensive, and inaccurate.” The tricky part, of course, is teaching the machines to recognize nuances, such as how two employees can have the same title but drastically different responsibilities.

采取分析师。有一些基线职责，如金融建模，所有金融分析师都可以预期知道如何做。但有些人有酌情决定，包括采摘股票，该股票在市场上发挥了更高的薪水。

Machines are far from perfect, but letting them handle the process can help employers stay on the right side of the law. Zoia says CompIQ’s system analyzes and flags jobs where there may be pay discrepancies, a feature he expects will be offered by any competitors that enter the market.

除了新的股权法律外，雇主还向雇主提出了非法候选人的候选人，以便在上一份工作中提出工作。Salary.com的Reynolds说，询问问题不是最好的练习 - 但很多雇主仍然想知道。

“如果你不能问一名员工现在所做的东西，你需要一种替代方法来收集这种数据，”她说。“无论是来自聚合数据提供商还是调查提供商，您都希望确保您获得有效数据，这些数据不会受到在某个网站上输入自己的数据的员工的影响。”

AI Systems还有助于艾伦约翰逊呼叫某些组织中存在的“权利文化”的赔偿顾问。亚慱体育app怎么下载

“There will be charts and graphs that show, ‘Geez, Alan has been underperforming for two years — why is he paid so well?’” says Johnson. When he has flagged certain employees as being under- or overpaid, clients will acknowledge it, he says, but are often unwilling to address it due to corporate culture, politics, or an “out of sight, out of mind” attitude. AI systems “will make it harder to hide.”

Reynolds notes that such systems can also flag when superstar employees are significantly underpaid versus the market — making them a potential flight risk. Predictive analytics tools also can enable companies to identify which jobs will be in demand in the coming years, allowing them to set salary increases accordingly to stay competitive when it comes to retaining talent.

所以有什么问题applications?

They’re designed by humans.

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Soumendra Mohanty is an AI technology senior executivefor IT company LTI. He points out that the purpose of algorithms is to cut out subjectivity — but they aren’t foolproof. “The danger is, we build the algorithms, we feed the data, and we train the algorithm to do the job,” Mohanty wrote in an email to亚博赞助欧冠。“因此，人类偏见的真正问题蔓延到算法中。”

为他的一部分，约翰逊对这些新技术提供的承诺感兴趣，称人力资源领域已经逾期，以便在薪酬和职业周围更好地分析。但如果早期的兴奋可能会被覆盖，他会奇怪。

“I think it’ll be helpful, but I’m not sure it’ll be that helpful in determining the dollar amount every single person gets,” he says. “It may be a case of using a cannon to kill a flea. Many times it’s not the facts, it’s the will to do something. Do you need AI to figure it out? Sometimes you do. But it’s like Weight Watchers. We all should just eat less. But if WW or AI gets people excited to do it, then okay.”