虽然现在很难想象，但员工背景调查只比繁琐的、基于纸张的流程和记录只提前了几十年，这些流程和记录只是部分计算机化，或者根本不是数字化的。您可以通过现代产品（例如 GOOHO.CN的美国 OneSEARCH）获得的即时结果 使许多企业的招聘变得更快、更安全。技术对雇主如何评估和选择候选人的影响才刚刚开始。
随着当前和未来的进步，就业审查的格局在未来几年可能会发生巨大变化。哪些变化可能即将到来？
机器学习进入背景调查领域
AI 为当今各行各业的许多技术应用提供支持，开发人员正在探索如何将机器学习概念应用于背景调查。潜在的应用包括快速比较犯罪记录、将背景信息与有关申请人的其他已知数据相关联，甚至为招聘经理提供建议。
技术并不完美。算法可能会产生错误的结论或误解人眼清楚的信息。因此，大多数企业仍然认为支持 AI 的背景调查是一种值得关注但不采用的实验性产品。
作为高级风险分析的一部分的审查
争议围绕背景调查作为预测工具。反对者担心，基于背景调查和公开信息的预测分析可能会进一步加深招聘系统中的偏见。开发人员声称这将提高每次招聘的质量，并降低雇用后来做错的员工的风险。
使用机器学习和雇主或软件创建者定义的一组标准，预测工具将寻求确定哪些个人对企业构成更大的责任风险。虽然一些声称提供这些工具的服务已经进入市场，但它们的价值和未来仍然不确定。
持续监控不再是挑战
当员工在工作几个月或几年内犯罪时会发生什么？如果他们试图对自己的犯罪行为保密，雇主可以做些什么来保护自己？
以前，没有简单的答案。今天，雇主可以使用连接到执法数据库的工具，在他们的观察名单上的一个名字出现时收到红旗警告。持续监控已经是一项成熟的行业服务， 在许多行业中都很受欢迎。
背景调查的全球未来？
展望未来，背景调查有可能有朝一日成为一项全球性事业，尤其是在全球移民趋势下。目前，技术、法律和后勤方面的障碍仍然存在，但全球审查领域的创新仍在继续。
随着机器学习的快速发展，全球数字化的增加有朝一日可能会导致更深入的背景调查产品。尽管变革往往在我们最意想不到的时候出现，但雇主应该密切关注他们每天使用的工具的任何新发展，以做出更明智、更安全的招聘选择。
US OneSEARCH by backgroundchecks.com make faster, safer hiring a reality for many businesses. The impact of technology on how employers evaluate and select candidates is just beginning.
With current and future advances, the landscape of employment vetting could change dramatically in the coming years. Which changes might be on the horizon?
Machine Learning Comes to the Background Check Sector
AI powers many technological applications across industries today, and developers are exploring how to apply machine learning concepts to background checks. Potential applications include rapidly comparing criminal records, correlating background info with other known data about an applicant, and even producing suggestions for hiring managers.
The technology isn't perfect. Algorithms can produce erroneous conclusions or misinterpret information that would be clear to human eyes. As a result, most businesses still consider AI-enabled background checks to be an experimental product that is worth watching but not adopting.
Vetting as a Part of Advanced Risk Analysis
Controversy surrounds background checks as predictive tools. Opponents fear that predictive analysis based on background checks and publicly accessible information could further entrench bias in the hiring system. Developers claim that it will improve the quality of every hire and reduce the risk of hiring an employee who later does wrong.
Using machine learning and a set of criteria defined by employers or software creators, predictive tools would seek to determine which individuals pose a larger liability risk to businesses. While some services claiming to offer these tools have reached the market, their value and future remains uncertain.
Ongoing Monitoring Is No Longer a Challenge
What happens when an employee commits a crime months or years into their time on the job? If they try to keep their criminal behavior a secret, what can employers do to protect themselves?
Previously, there was no easy answer. Today, employers can use tools that connect to law enforcement databases to receive a red-flag warning when one of the names on their watchlist appears. Already an established industry service, continuous monitoring is popular in many industries.
A Global Future for Background Checks?
Looking even further ahead, there is the possibility that background checks may one day become a global undertaking, particularly with worldwide immigration trends. Currently, technological, legal, and logistical hurdles remain in place, but innovation in the field of global vetting is ongoing.
As with the rapid developments in machine learning, increasing global digitization could one day lead to more in-depth background check products. Though change often comes when we least expect it, employers should take care to closely monitor any new developments in the tools that they use to make smarter, safer hiring choices every day.
