人工智能如何改变人才获取 How Artificial Intelligence Is Changing Talent Acquisition现在大家都在关注招聘AI，并就如何改变招聘方式进行了大量的讨论。招募人工智能是下一代软件，旨在改进或自动化招聘工作流程的某些部分。
作者：Ji-A Min
人工智能对招聘的兴趣已经由三大趋势引发
经济的改善：最近的经济收益创造了一个候选人驱动型市场，这使得人才竞争比以往更加激烈。这一竞争只会继续增加 - LinkedIn调查的 56％的人才招聘领导者认为他们的招聘数量将在2017年增长。
对更好技术的需求：虽然人才招聘预计会增加，但是66％的人才招聘负责人表示他们的招聘团队将保持相同规模甚至缩小规模。这意味着时间有限的招聘人员需要更好的工具来有效地简化或自动化他们的工作流程的一部分，理想情况下用于最耗时的任务。
数据分析的进步：随着技术变得快速和成本效益足以收集和分析大量数据，人才招聘领导者越来越多地要求他们的招聘团队展示基于数据的雇佣质量指标，如新员工的表现和营业额。
人工智能在招聘中越来越受欢迎，这为招聘人员提高他们的能力提供了令人兴奋的机会，但同时也存在很多关于如何最佳利用人才的困惑。
为了帮助您理解这一切，以下是招聘人工智能最有前途的三个应用程序。
应用＃1：AI用于候选人采购
候选人采购仍然是一个主要的招聘挑战：最近的一项调查发现，46％的人才招聘领导表示他们的招聘团队正在为吸引合格的候选人而奋斗。
候选人采购人工智能技术可以搜索人们离线的数据（例如简历，专业投资组合或社交媒体档案），以找到符合您工作要求的被动候选人。
这种用于招聘的AI可以简化采购流程，因为它可以同时搜索多个候选人来源。这取代了自己手动搜索它们的需求，并可能节省每个请求的小时数。您节省采购的时间可以用来吸引，预选和面试最强大的候选人。
应用#2：人工智能进行候选人筛选
当您收到的75-88％的简历不合格时，很容易明白为什么简历筛选是招聘中最令人沮丧和耗时的部分。对于零售和客户服务等大批量招聘，大多数招聘团队没有时间手动筛选他们每个公开角色收到的数百到数千份简历。
AI筛选旨在自动执行简历筛选流程。这种智能筛选软件通过使用岗位聘用数据（例如业绩和营业额）为新申请人提供招聘建议，为ATS增添了功能。
它通过应用所学到的关于现有员工的经验，技能和其他资质的信息来自动筛选和评分新候选人，从而提出这些建议。这种类型的技术还可以通过使用关于以前的雇主和候选人的社交媒体档案的公共数据源来丰富简历。
AI进行简历筛选可实现低价值，重复性任务，并允许招聘人员将时间重点放在更高价值的优先事项上，如与候选人交谈并与其进行交流以评估他们的适合度。
应用＃3：AI用于候选人匹配
与采购相比，候选人匹配可能是一个更大的挑战：52％的招聘人员表示，他们工作中最难的部分是从大型申请人池中确定合适的人选。
用于候选人匹配的AI使用一种算法来识别打开的请求的最强匹配。匹配算法分析候选人的个性特征，技能和工资偏好等多种数据来源，根据工作要求自动评估候选人。
例如，LinkedIn求职公告通过将求职者描述中的技能与其LinkedIn个人资料中的申请人技能进行匹配来对候选人进行排名。人才市场使用匹配算法来匹配候选人社区以开放角色。这些人才市场通常迎合特定的候选技能，如软件开发或销售。
人工智能匹配用于从那些已经加入并且正在积极寻找新角色或者对新机会非常开放的人中找出最合格的候选人。这意味着招聘人员不需要浪费时间来吸引那些对新角色不感兴趣的被动应聘者。
关于人工智能的力量，让候选人与工作岗位相匹配的不同观点，请参阅“ 尽管您阅读或听取的内容，采购活动和确实如此”。
AI和招聘的未来
专家预测人工智能招聘会转变招聘人员的角色。由于低价值，耗时的招聘任务通过人工智能技术变得简化和自动化，招聘人员的角色有可能变得更具战略性。
了解AI如何提高其能力的招聘人员将通过在采购，简历筛选和候选人匹配方面节省几十个小时，从而提高效率。
人工智能招聘承诺释放招聘人员与候选人交流的时间，以确定合适人选，并确定候选人的需求并希望说服他们担任角色。它有可能授权他们与招聘经理和人才招聘领导者合作，根据未来增长和收入计划积极的招聘举措，而不是反应性回填。
了解如何最好地利用这项新技术的招聘人员将获得更高的KPI，如更高的招聘质量和更低的营业额。
以上由AI翻译完成。供参考
How Artificial Intelligence Is Changing Talent Acquisition
AI for recruiting is on everyone’s mind these days with a lot of talk on how it’s going to transform recruiting. Artificial intelligence for recruiting is the next generation of software designed to improve or automate some part of the recruiting workflow.
Interest in AI for recruiting has been sparked by three major trends:
The improving economy: The recent economic gains have created a candidate-driven market that’s made competing for talent tougher than ever. This competition will only continue to increase – 56% talent acquisition leaders surveyed by LinkedIn believe their hiring volume will grow in 2017.
The need for better technology: Although hiring is predicted to increase, 66% of talent acquisition leaders state their recruiting teams will stay the same size or even shrink. This means time-constrained recruiters need better tools to effectively streamline or automate a part of their workflow, ideally for tasks that are the most time-consuming.
The advancements in data analytics: As technology becomes fast and cost-effective enough to collect and analyze vast quantities of data, talent acquisition leaders are increasingly asking their recruiting teams to demonstrate data-based quality of hire metrics such as new hires’ performance and turnover.
The growing popularity of AI for recruiting represents exciting opportunities for recruiters to enhance their capabilities but there’s also a lot of confusion about how to best leverage it.
To help you make sense of it all, here are the three most promising applications for AI for recruiting.
Application #1: AI for candidate sourcing
Candidate sourcing is still a major recruiting challenge: a recent survey found 46% of talent acquisition leaders say their recruiting teams struggle with attracting qualified candidates.
AI for candidate sourcing is technology that searches for data people leave online (e.g., resumes, professional portfolios, or social media profiles) to find passive candidates that match your job requirements.
This type of AI for recruiting streamlines the sourcing process because it can simultaneously search through multiple sources of candidates for you. This replaces the need to manually search them yourself and potentially saves you hours per req. The time you save sourcing can be spent attracting, pre-qualifying, and interviewing the strongest candidates instead.
Application #2: AI for candidate screening
When 75-88% of the resumes you receive are unqualified, it’s easy to see why resume screening is the most frustrating and time-consuming part of recruiting. For high-volume recruitment such as retail and customer service roles, most recruiting teams just don’t have the time to manually screen the hundreds to thousands of resumes they receive per open role.
AI for screening is designed to automate the resume screening process. This type of intelligent screening software adds functionality to the ATS by using post-hire data such as performance and turnover to make hiring recommendations for new applicants.
It makes these recommendations by applying the information it learned about existing employees’ experience, skills, and other qualifications to automatically screen and grade new candidates. This type of technology can also enrich resumes by using public data sources about previous employers and candidates’ social media profiles.
AI for resume screening automates a low-value, repetitive task and allows recruiters to re-focus their time on higher value priorities such as talking and engaging with candidates to assess their fit.
Application #3: AI for candidate matching
Candidate matching can be an even bigger challenge than sourcing: 52% of recruiters say the hardest part of their job is identifying the right candidates from a large applicant pool.
AI for candidate matching uses an algorithm to identify the strongest matches for your open req. Matching algorithms analyze multiple sources of data such as candidates’ personality traits, skills, and salary preferences to automatically assess candidates against the job requirements.
For example, a LinkedIn job posting ranks candidates by matching the skills on your job description to applicants’ skills on their LinkedIn profiles. Talent marketplaces use matching algorithms to match their community of candidates to open roles. These talent marketplaces usually cater to specific candidate skill sets such as software development or sales.
AI for matching is used to identify the most qualified candidates from those who have opted-in and are either actively looking for a new role or are very open to a new opportunity. This means recruiters don’t need to waste time trying to attract passive candidates who just aren’t interested in a new role.
“人员分析现在可以成为战略性竞争优势”
工业工程师弗雷德里克泰勒在1911年发表了他的报告“ 科学管理”，该报告研究了钢厂工厂工人的流动和行为，从而开始了这一趋势。此后，公司已经部署了数千次参与调查，研究了最高领导者的特征，对留存率和营业额进行了无数次评估，并建立了大量的人力资源数据仓库。所有这些努力都是为了弄清楚“我们能做些什么来让我们的人们获得更多收益？”
那么现在这个域被称为人们的分析，它已经成为一个快速增长的核心业务举措。一项题为“ 高影响力人物分析 ”的研究报告由Deloitte在去年11月由Bersin完成，发现69％的大型组织拥有人员分析团队，并积极构建与人员相关数据的综合存储。
为什么增长和为什么业务势在必行？几个技术和商业因素相互碰撞使这个话题变得如此重要。
首先，组织拥有比以往更多的与人员相关的数据。由于办公生产力工具，员工证章阅读器，脉搏调查，集成的企业资源规划系统和工作中的监控设备的激增，公司拥有大量关于员工的详细数据。
公司现在知道人们与谁交流，他们的地点和旅行时间表，工资，工作经历和培训计划。内置于电子邮件平台中的组织网络分析的新工具可以告诉正在与谁交流的领导者，用于音频和面部识别的新工具识别谁处于压力之下，以及摄像机和热传感器甚至可以确定人们在他们身上花费了多少时间书桌。
可以认为，这些信息大部分都是保密和私密的，但大多数员工并不介意获取这些数据的组织，只要他们知道正在改进他们的工作体验，正如2015年会议委员会的研究所显示的那样，Big数据并不意味着大 哥哥。虽然从5月25日起可执行的欧盟通用数据保护条例标准将会将隐私权和治理责任放在人力资源部门，但雇主正在加紧处理这些数据并小心处理这些数据。
其次，作为获得所有这些数据的结果，公司现在可以学习重要而有力的事情。不仅高管们被迫就多元化，性别薪酬公平和营业额等议题进行报告，而且他们现在还可以使用人员分析来了解生产力，技能差距和长期趋势，这些可能会威胁或创造业务风险。
例如，一个组织发现欺诈和盗窃事件是“具有传染性”，导致同一楼层的其他员工在一定距离内出现类似的不良行为。另一种方法是使用情绪分析软件来衡量组织中的“情绪”，并根据他们的沟通模式来识别具有高风险项目的团队。
许多组织现在都在研究营业额，甚至可以通过监测电子邮件和社交网络行为来预测它，从而使管理人员能够在辞职前指导高绩效员工。组织现在使用分析和人工智能或人工智能来解码职位描述，识别造成偏倚招聘池的单词和短语，并防止性别和种族多样性。制造商使用人员分析来识别可能发生事故的员工，而咨询公司可以预测哪些人可能会因过多的旅行而被烧毁，而汽车公司现在知道为什么某些团队按时完成项目，而其他人则总是迟到。
因此，人工智能进入领域，给予它更多的权力和规模。一个新的基于人工智能的分析工具会向管理人员发送匿名电子邮件，询问简单问题以评估管理技能。通过其精心设计的算法，它为管理人员提供了一套无需赘述的建议，并在短短三个月内将管理效率提高了8％。
据Sierra-Cedar 2017人力资源系统调查显示，对于人力资源部门而言，人员分析现在是公司希望替换或升级人力资源软件的首要原因。
但对于首席执行官，首席财务官和首席运营官来说，这更重要。当一个销售团队落后于其配额实现或者商店的销售数字落后时，为什么领导者不会问“我们可能能够解决的团队中的人员，实践和管理者有什么不同？”或者甚至更大问题是“如果我们想通过收购德国的某家公司来发展我们的业务，文化和组织的影响会是什么？”这些关键的战略问题都可以通过人员分析来解决。
这门学科的历史是战术性的，有点神秘。多年来，工业心理学家领导了这项工作，主要关注员工敬业度和营业额。然而，今天，该行业正在采取新的行动，将其精力重新集中在运营，销售，风险和绩效指标上。技术工具在这里，公司已经有人工智能工程师准备以强大而有预见性的方式分析数据。分析人士表示，这个领域将会持续增长，请记住，对于大多数企业而言，劳动力成本是资产负债表中最大和最可控制的支出。
底线很明显：人们的分析现在可以成为战略竞争优势。专注于这一领域的公司可以出租，淘汰和淘汰竞争对手。
以上由AI自动翻译。
Fredrick Taylor, an industrial engineer, started this trend in 1911 when he published his report Scientific Management, which studied the movement and behaviour of factory workers in steel mills. Since then companies have deployed thousands of engagement surveys, studied the characteristics of top leaders, done countless reviews of retention and turnover, and built massive human resources data warehouses. All in an effort to figure out “what can we do to get more out of our people?”
Well now this domain is called people analytics and it has become a fast-growing, core-business initiative. A study, entitled High-Impact People Analytics and completed last November by Bersin by Deloitte, found that 69 per cent of large organisations have a people analytics team and are actively building an integrated store of people-related data.
Why the growth and why the business imperative? Several technical and business factors have collided to make this topic so important.
Firstly, organisations have more people-related data than ever before. Thanks to the proliferation of office productivity tools, employee badge readers, pulse surveys, integrated enterprise resource planning systems and monitoring devices at work, companies have vast amounts of detailed data about their people.
Companies now know who people are communicating with, their location and travel schedules, their salary, job history and training plans. New tools for organisational network analysis, built into email platforms, can tell leaders who is communicating with whom, new tools for audio and facial recognition identify who is under stress, and video cameras and heat sensors can even identify how much time people spend at their desks.
It could be argued that much of this information is confidential and private, but most employees don’t mind organisations capturing this data, as long as they know it is being done to improve their work experience, as shown in 2015 Conference Board research, Big Data Doesn’t Mean Big Brother. While European Union General Data Protection Regulation standards, enforceable from May 25, will put the burden of privacy and governance on HR departments, employers are stepping up to this and treating such data with great care.
Secondly, as a result of having access to all this data, companies can now learn important and powerful things. Not only are executives being forced to report on topics such as diversity, gender pay equity and turnover, but they can also now use people analytics to understand productivity, skills gaps and long-term trends that might threaten or create risk in their business.
One organisation, for example, found incidents of fraud and theft were “contagious”, causing similar bad behaviour among other employees on the same floor within a certain distance. Another is using sentiment analysis software to measure “mood” in the organisation and can identify teams with high-risk projects just from the patterns of their communication.
Many organisations now study turnover and can even predict it before it occurs by monitoring email and social network behaviour, enabling managers to coach high performers before they resign. Organisations now use analytics and artificial intelligence or AI to decode job descriptions, identifying words and phrases that create biased recruitment pools and prevent gender and racial diversity. Manufacturers use people analytics to identify workers who are likely to have accidents, while consulting firms can predict who is likely to be burnt out from too much travel and automotive companies now know why certain teams get projects done on time when others are always late.
AI is, therefore, entering the domain, giving it even more power and scale. A new AI-based people analytics tool sends anonymous emails to a manager’s peers asking simple questions to assess managerial skills. Through its carefully designed algorithms, it gives managers an unthreatening set of recommendations and has improved managerial effectiveness by 8 per cent in only three months.
For human resources departments, people analytics is now the number-one reason companies want to replace or upgrade their HR software, according to the Sierra-Cedar 2017 HR Systems Survey.
But for chief executives, chief financial officers and chief operating officers, it’s even more important. When a sales team is behind its quota attainment or a store’s sales numbers fall behind, why wouldn’t a leader ask “what’s different about the people, practices and managers at those teams that we may be able to address?” Or an even bigger question is “if we want to grow our business by acquiring a given company in Germany, what will the cultural and organisational impact be?” These critical strategic questions can all be answered by people analytics.
The history of this discipline is tactical and somewhat arcane. For years industrial psychologists led the effort and focused primarily on employee engagement and turnover. Today, however, the industry is taking on a new light, refocusing its energy on operational, sales, risk and performance measures. The technology tools are here and companies have AI engineers ready to analyse the data in a powerful and predictive way. And analysts say this domain will grow for years to come; remember that for most businesses, labour costs are the largest and most controllable expense on the balance sheet.
The bottom line is clear: people analytics can now become a strategic competitive advantage. Companies that focus in this area can out-hire, out-manage and out-perform their competitors.
AI 人工智能中对HR的常见术语解释，没事可以了解下在工作环境中准备人工智能对于技术厌恶的招聘团队来说是一个令人望而生畏的。
对于那些探索他们的业务意味着什么，这里有8个基本的解释开始：
算法：算法是在解决问题或计算中应遵循的一组规则。算法需要在招聘过程中生成大量数据，并将其转换为HR可用于候选人选择的信息。在之前的一项研究中，通过算法招聘的候选人比人力资源招聘的人员长15％。算法有助于提高候选人的选择并减少不良招聘的可能性。
人工智能（AI）：人工智能（AI）通常被称为“第四次工业革命”，它是一种能够模仿智能人类行为的机器，其中包括做决策和执行基本任务，如解决问题，计划和学习。AI可以自动执行重复和平凡的管理任务，包括整个招聘过程中的筛选和申请人更新。这也是聊天机器人的兴起和视频放映的使用背后的原因。
聊天机器人：聊天机器人的简称，聊天机器人在人才获取方面越来越多。与苹果的Siri或亚马逊的Alexa一样，招聘中的聊天机器人使用人工智能（例如，机器学习 - 见下文）来理解问题并作出回应。Chatbots可以在不同的平台上使用，包括电子邮件，消息应用程序和通过您的申请人跟踪软件。Chatbots旨在模拟与您就业网站的访问者的对话，并正在迅速成为高容量招聘的基本技术工具。聊天机器人有效地使用，为您的招聘过程添加更吸引人的互动元素。今年早些时候进行的一项调查发现，在申请过程中，超过一半的候选人愿意与聊天机器人进行互动。
游戏化：游戏化将游戏的常见元素应用于其他在线活动领域，包括市场营销。在招聘过程中，毕业生雇主经常使用劳埃德银行集团，德勤和普华永道倍受青睐的Multipoly，以吸引年轻人才，创造更具吸引力的候选人经验。通过人力资源技术将游戏化融入您的招聘流程中。
机器学习：类似于人工智能，机器学习为AI提供了更智能的算法。在招聘中，机器学习可以减少您的聘用时间，并用于自动化候选人筛选，通常利用招聘分析中与最成功的人员相关的数据。人力资源软件中复杂的机器学习算法可用于通过语言选择甚至面部表情来评估候选人的潜在文化适应性。
人员分析：人员分析将数据和分析结合起来，深入了解与员工相关的一系列问题，包括领导力，绩效管理和招聘。要了解更多信息，请参阅我们以前的文章，其中提供了有关人员分析的更详细的介绍。
情绪分析：这可能不是你熟悉的词，但情绪分析解释了语言对人的影响，无论是消极的，积极的还是中立的。在招聘时可以用来分析你的工作岗位的措辞的影响。例如，去年我们报道说，在社交媒体平台Buffer的“工作岗位”中用'开发者'取代'黑客'这个词，看到女性候选人申请空缺的人数有所增加。在招聘软件中的人力资源分析提供了更多的洞察力，如何使用特定的话可以阻止人才适用于您的工作。使用情感分析的软件也可以提出更合适的词汇来吸引更多元化的人才库。
图灵测试：图灵测试是由科学家阿兰·图灵（Alan Turing）在1950年设想的，前提是“机器可以想象？今天，它指的是人工智能的潜力，以说服人们，而不是一个机器与人互动。其中最成功的例子是第三次获得2017年Loebner奖（基于图灵测试）的Mitsuku聊天机器人，但尚未说服评委是人类。 随着工作场所的自动化程度的提高，这是一个与AI有关的术语。
以上由AI 自动翻译。
Algorithms : An algorithm is a set of rules to be followed in a problem solving situation or calculation. Algorithms take large amounts of data generated during the hiring process and transform it into information that HR can use in candidate selection. In a previous study, candidates recruited via algorithms remained in their job 15% longer than those hired by HR. Algorithms help to improve candidate selection and reduce the potential for a bad hire.
Artificial Intelligence (AI) : Often referred to as the ‘fourth industrial revolution’, artificial intelligence (AI) is a machine that is capable of imitating intelligent human behaviour, which includes making decisions and performing basic tasks such as problem solving, planning and learning. AI automates repetitive and mundane admin tasks, including screening and applicant updates throughout the hiring process. It is also behind the rise in chatbots and the use of video screening.
Chatbots : Short for chat robot, chatbots are becoming more prolific in talent acquisition. Like Apple’s Siri or Amazon’s Alexa, chatbots in recruitment use artificial intelligence (eg, machine learning - see below) to comprehend questions and respond. Chatbots can be used across different platforms, including e-mail, messaging apps and through your applicant tracking software. Chatbots are designed to simulate conversations with visitors to your careers site and are rapidly becoming an essential tech tool for high volume recruitment. Used effectively, chatbots add a more engaging and interactive element to your hiring process. A survey carried out earlier this year found that over half of candidates are comfortable interacting with chatbots during the application process.[1]
Gamification : Gamification applies the common elements of game playing to other areas of online activity, including marketing. In recruitment it is frequently used by graduate employers, including Lloyds Banking Group, Deloitte and in PwC’s popular Multipoly to attract young talent and create a more engaging candidate experience. Incorporate gamification into your recruitment process through your HR technology.
Machine learning : Similar to artificial intelligence, machine learning provides AI with the algorithms that make it more intelligent. In hiring, machine learning can reduce your time to hire and is used to automate candidate screening, often utilising the data available in your recruitment analytics relating to your most successful hires. Sophisticated machine learning algorithms in HR software can be used to evaluate the potential cultural fit of a candidate through language choice and even facial expressions.
People analytics : People analytics combines data and analysis to gain insight into a range of issues related to your employees, including leadership, performance management and recruitment. For more insight, please see our previous article which provides a more detailed introduction to people analytics.
Sentiment analysis : It may not be a term you are familiar with but sentiment analysis interprets the effect that language has on people, whether negative, positive or neutral. In recruitment it can be used to analyse the impact of the wording of your job posts. For example, last we year we reported that by replacing the word ‘hacker’ with ‘developer’ in their job posts, social media platform Buffer saw an increase in the number of female candidates applying to their vacancies. HR analytics in your recruitment software provide more insight into how the use of specific words can deter talent from apply to your jobs. Software which uses sentiment analysis can also suggest more suitable words to attract a more diverse talent pool.
The Turing Test : The Turing Test was conceived by scientist Alan Turing in 1950 based on the premise 'can machines think?' Today it refers to the potential for artificial intelligence to convince people they are interacting with a person rather than a machine. One of the most successful examples is the Mitsuku chatbot which has been awarded the 2017 Loebner Prize[2] (based on the Turing Test) for the third time but has yet to convince the judges it is human. It's a term you may hear more of in relation to AI as automation in the workplace rises.
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