人力资源机器_人力资源部门的机器学习和AI

介绍 (Introduction)

Deep learning and AI have been drastically changing industries such as healthcare, financial services, and retail with many companies welcoming new technologies. However, Human Resources (HR) departments have been met with more challenges in integrating intelligent systems into their workflows.

深度学习和AI极大地改变了医疗，金融服务和零售等行业，许多公司欢迎新技术。 但是，在将智能系统集成到他们的工作流程中，人力资源(HR)部门面临着更多的挑战。

HR Departments are tasked with managing the organization’s employees — hiring, firing, resolving disputes, payroll, benefits, and more. Many of these tasks seem ripe for automation with machine learning, however, they are also often subjective, and handing over the reigns poses interesting ethical challenges.

人力资源部门的任务是管理组织的员工-雇用，解雇，解决纠纷，薪资，福利等。 对于通过机器学习进行自动化，这些任务中的许多任务似乎已经成熟，但是它们也常常是主观的，交出统治带来了有趣的道德挑战。

员工招聘 (Employee Hiring)

The hiring process is laborious and expensive. From reviewing resumes, interviewing, and training new employees, hiring new employees can carry a large cost to organizations outside of the new employee’s salary. This cost is generally worth it, however, because making the wrong decision can cost even more money if the employee must be let go and the process started again. Not only do the costs of hiring need to be incurred a second time, but lost production and the time it takes the new employee to ramp up to full production will also be present.

招聘过程既费力又昂贵。 通过审查简历，面试和培训新员工，雇用新员工可能会使组织承担新员工薪水以外的大量费用。 但是，这笔费用通常是值得的，因为如果必须放开员工并重新开始流程，那么做出错误的决定可能会花费更多的金钱。 不仅需要第二次产生雇用成本，而且还会出现生产损失以及新员工逐步投入全面生产所需的时间。

Because of this, many companies have looked towards deep learning to provide solutions in reducing the cost of hiring employees and increasing the quality of employees hired. However, these attempts have not always gone as planned.

因此，许多公司都希望通过深度学习来提供解决方案，以降低雇用员工的成本并提高雇用员工的素质。 但是，这些尝试并非总是按计划进行。

From 2014 to 2018, a team at Amazon built systems to review applicants' resumes in an effort to streamline the process of recruiting top talent. In order to train their algorithm, the team compiled a training dataset using resumes submitted to the organization over the prior ten years.

从2014年到2018年，亚马逊的团队构建了系统来审查申请人的简历，以简化招聘顶尖人才的过程。 为了训练他们的算法，团队使用了过去十年中提交给组织的简历来编译了训练数据集。

Amazon had hoped that this system would drastically reduce the time it took to identify top talent out of the applicant pool by automatically identifying the top x number of applicants. However, they later discovered that the system was favoring male over female applicants. This was because more male than female job seekers submitted resumes to Amazon, creating a biased and skewed dataset.

亚马逊曾希望，该系统能够通过自动识别申请人数的前x名，从而大幅减少从申请者中识别顶尖人才所需的时间。 但是，他们后来发现，该系统比男性申请人更偏爱男性。 这是因为向亚马逊提交简历的男性求职者多于女性，从而形成了有偏见和偏颇的数据集。

Creating unbiased hiring systems can be a difficult task. Since most companies rarely have exactly 50% male employees and 50% female employees, the model can often identify factors that it thinks are most telling of a good hire but are actually not considered by the hiring managers.

建立公正的招聘系统可能是一项艰巨的任务。 由于大多数公司很少有正好有50％的男性雇员和50％的女性雇员，因此该模型通常可以确定它认为最能说明一个好的员工但实际上未被招聘经理考虑的因素。

In order to create accurate hiring decisions and candidate ranking systems, care must be taken in assembling the datasets for training to eliminate unwanted behavior. In addition, it may be viable to hardcode the model to disregard certain features such as name, gender, and race.

为了创建准确的招聘决定和候选人排名系统，在组装数据集进行训练时必须格外小心，以消除不必要的行为。 此外，对模型进行硬编码以忽略某些功能(例如姓名，性别和种族)可能是可行的。

每小时排程 (Hourly Scheduling)

HR Teams managing hourly employees have a daunting task when creating schedules. When your employees are not all full-time with a consistent schedule, scheduling conflicts often arise. Because of the unpredictable work schedule, a key function of HR managers (and often general managers) is managing time-off and shift-change requests.

管理小时员工的人力资源团队在制定时间表时会面临艰巨的任务。 如果您的员工不是全职的，并且时间表一致，则经常会发生时间表冲突。 由于工作计划无法预测，因此人力资源经理(通常是总经理)的一项关键职能是管理休假和轮班变更请求。

If you ask many restaurant or retail store managers, scheduling and it’s related tasks often take up a large portion of their workday. However, deep learning systems are starting to take this burden.

如果您向许多餐厅或零售商店的经理咨询，则日程安排及其相关任务通常会占用他们的大部分工作时间。 但是，深度学习系统开始承担这一负担。

Automated systems can analyze these requests and automatically approve or deny them based on predefined business rules on a personal level. For example, many organizations that employ part-time shift workers do not allow their employees to work 40 or more hours in a given week. If a shift-change request puts one employee above 40 hours for the week, the system would decline the request without any human intervention.

自动化系统可以分析这些请求，并基于个人级别上的预定义业务规则自动批准或拒绝它们。 例如，许多雇用兼职轮班工人的组织不允许雇员在给定的一周内工作40个小时或更长时间。 如果轮班变更请求使一名员工一周的工作时间超过40小时，则系统将在没有任何人工干预的情况下拒绝该请求。

These systems become even more powerful when combined with predicted demand information. Accurately predicting when additional employees are needed and adjusting schedules and time-off requests accordingly can provide increases in the efficiency of employee management by saving on labor costs when demand is low and assuring adequate employees are working when demand is high.

当与预测的需求信息结合使用时，这些系统将变得更加强大。 准确地预测何时需要更多员工，并相应地调整计划和休假要求，可以通过在需求低迷时节省劳动力成本，并在需求高迷时确保有足够的员工来工作，从而提高员工管理效率。

While these systems can drastically improve employee management and reduce the workload for managers, it could hurt employee morale. Often times time-off and shift-change requests can be personal in nature. If an automated system denies a time-off request for an important event, the employee may grow resentment towards the organization.

虽然这些系统可以大大改善员工管理并减少经理的工作量，但它可能会损害员工的士气。 通常，休假和换班请求本质上是个人的。 如果自动化系统拒绝重要事件的超时请求，则员工可能会对组织产生不满。

For automated scheduling systems to work, it is important to thoroughly define business rules and ensure there is a way for employees to receive an override of the model’s decision through their manager.

为了使自动调度系统正常工作，重要的是要彻底定义业务规则，并确保员工有办法通过其经理接收对模型决策的超越。

劳动力分析 (Workforce Analytics)

Analytics teams have allowed organizations to harness their data in ways never before allowing companies to make more informed decisions than ever before.

分析团队允许组织以前所未有的方式利用其数据，从而允许公司做出比以往任何时候都更明智的决策。

When we think about business data, we often picture them collecting data on their customers to better understand them. However, many organizations are also collecting data on their employees.

当我们考虑业务数据时，我们经常给他们描绘客户收集的数据以更好地理解它们。 但是，许多组织也在收集有关其员工的数据。

Tracking key metrics on an organization’s employees allows HR departments to better understand their workforce. Tracking employees’ sentiment, productivity, and connection to the organization empower HR departments to better allocate resources and improve the efficiency of their workforce. In addition, it allows predictive analytics models to identify employees at risk of leaving the organization or likely to be promoted.

跟踪组织员工的关键指标可以使人事部门更好地了解其员工队伍。 跟踪员工的情绪，生产力和与组织的联系使人力资源部门能够更好地分配资源并提高员工效率。 此外，它还允许预测分析模型识别可能离开组织或可能晋升的员工。

While workforce analytics may seem like an obvious choice for HR departments, employees may not have the same perspective. Distilling an employee into a series of numerical metrics can dehumanize the management process and make employees feel like nothing more than a number.

尽管人力资源分析对于人力资源部门来说似乎是一个显而易见的选择，但员工的看法可能并不相同。 将员工精简为一系列的数字指标可能会使管理流程失去人性化，并使员工感到无非是一个数字。

结论 (Conclusion)

Machine learning and AI have promising applications in human resources. However, switching from human to machine-driven management can cause major issues in employee morale within an organization. Do these technologies create a new age in productivity or remove the ‘human’ from HR? I’ll leave that up to you.

机器学习和AI在人力资源中具有广阔的应用前景。 但是，从人为管理切换到机器驱动管理可能会导致组织内员工士气出现重大问题。 这些技术是否创造了生产力的新时代，还是从人力资源中去除了“人”？ 我留给你。