{"id":257,"date":"2020-07-22T09:07:00","date_gmt":"2020-07-22T09:07:00","guid":{"rendered":"https:\/\/staging.neurolytics.ai\/?p=257"},"modified":"2024-05-27T12:33:04","modified_gmt":"2024-05-27T10:33:04","slug":"ai-helps-human-capital-and-recruitment-how-do-you-keep-it-human-en-nl","status":"publish","type":"post","link":"https:\/\/neurolytics.ai\/en\/ai-helps-human-capital-and-recruitment-how-do-you-keep-it-human-en-nl\/","title":{"rendered":"AI helps Human Capital and Recruitment: how do you keep it human? (EN, NL)"},"content":{"rendered":"\t\t
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Computers can process data better, faster,<\/span><\/i> and more accurately than humans and therefore help in the recruitment process. But that will only yield good results when systems are transparent, free of prejudice, and people-friendly. How does human-centered AI work in practice?<\/span><\/p>

Systems usually analyze data better and faster than humans and sometimes also data that cannot be perceived by the human eye. But crucially important in these analyzes: people ultimately make the decisions. In order to be able to make those decisions, everything depends on having insight into your own system. The lack of it is the reason there is criticism. Suppliers are not always transparent about how the system works and how they arrive at scores. This is because data enters a black box and results come out that are sometimes difficult to explain. To prevent this, it is important to understand and have insight into your system and all variables. It is advisable to use machine learning in an insightful way to check how the system uses your data and makes predictions about a person. For example, does the system use logical and scientifically validated variables from a dataset? At Neurolytics we notice that it helps enormously if you can explain to those involved exactly how a score came about and which variables contributed to it.<\/span><\/p>

Ultimately, the system will formulate criteria that everyone must meet and you will only get the same types; you don\u2019t want this. You run the risk that the \u2018strange birds\u2019 that deviate from this \u2013 the outliers in your data \u2013 will not be invited. While they can have enormous added value and add something new to your teams or corporate culture.<\/span><\/p>

In addition, the system does not have to create criteria, these criteria can be specified yourself. You can set criteria or a baseline based on your own company data and culture. This way you can adopt a future-oriented approach in a self-chosen direction. If you have insight into the state of your current workforce, you can think about where you want to be in the future. You can select new applicants based on behavioral characteristics of the mindset that fits the future vision of your organization; the place where you want to be in, for example, five years.<\/span><\/p>

Systems are as good as they are built and must be monitored properly. It is therefore important for decision-makers that they work with the right parties in which everything can be explained clearly and substantively. So am well aware of restrictions. After all, nothing is perfect. As an HR decision-maker, you will ultimately miss the boat if you do not start working with these new technologies, because they can simply process data better, faster, and more accurately than people. But the right choices have to be made.<\/span><\/p>

There are often arguments against new technologies because they are new and exciting, there may be teething problems or a system has not been set up correctly. On the one hand, because first movers are sometimes too early, so that the system is not yet optimal (or not properly constructed at all), and they also fail to indicate this because they want to sell. On the other hand, because it is compared to the classic model that people are used to and with which they have been working for decades. However, it is sometimes forgotten that there is a lot of bias in it and it can be very inefficient.<\/span><\/p>

The classic recruitment process with a lot of bias also takes a lot of time; Scan resumes and motivation letters, plan communication with candidates, and conduct interviews with candidates who sometimes do not fit and drop out. In this case, you cannot learn from the data and therefore cannot take any steps forward. Furthermore, due to the human bias that we all have as humans, recruiters often choose applicants \u2013 based on gut feeling \u2013 who resemble them or with whom they have a personal click and not directly the people who fit in with the organizational culture or the team. This can lead to unnecessary turnover.<\/span><\/p>

Inclusion and diversity are important topics here: you want to give everyone the same opportunity to use his or her potential. For example, there should be no bias in only hiring white Western European thirties. You want to simulate a heterogeneous group and diversity. This can be done through solutions that disregard age, gender, and ethnicity, and that looks objectively at work preferences, skills, and human behavior. With new technology, unbiased insight can be obtained in, among other things, engagement with the purpose and culture of an organization, motivation, self-assurance, frustration, stress, and whether specific activities provide energy or cost.<\/span><\/p>

Another factor is that there are often many position changes in the HR world, as a result of which the accumulated knowledge also shifts. Motivation letters and resumes are not predictive of job success, and nearly one in three hires are considered unsuccessful in retrospect. That is precious. The costs for the throughput of employees are on average twice an annual salary, throughout the entire process of selection, interviews, onboarding, training, energy and time of colleagues, administrative costs, etc. You notice that modern decision-makers understand this well and are now looking for the right new technologies to help their recruiters and to use time and money more efficiently. Recent studies, such as <\/span>this one<\/span><\/a> and <\/span>this one<\/span><\/a>, has found that nearly 80 percent of CEOs\u2019 biggest concerns are related to human capital and the implementation of new technology. This will become even more important due to COVID-19 and the upcoming recession.<\/span><\/p>

In addition, more than 45 percent of jobs will be automated and changed in the coming years \u2013 and that is still conservatively estimated. At the same time as many existing functions are discontinued, new ones with different requirements are created. When re-skilling people, or looking at which skills are important in the future, social-emotional skills should be considered in particular. Computers can collect and process data much better than we humans, but we as humans remain unique in our soft skills, the warm human side. Employees will therefore spend more time on tasks that machines\/computers can do less and so these soft skills of people are becoming increasingly important. Specific activities such as logical reasoning and the use of empathy and creativity, as well as managing, applying your expertise, dealing with work pressure and stress, the culture and communication with others, and motivation and engagement with the purpose of an organization. Here too we are still dealing with a focus on hard skills instead of the mindset. Especially with the new generations who have grown up digitally, more attention will be paid to the mindset and soft skills when selecting new talent, because this will make more of a difference.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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What does this mean for practice?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Be aware of the change and digitization that is accelerating. Especially in times of recession, the right people and investments in new technologies (which reduce operational costs) make the difference.<\/span><\/p>

New technologies must be well-founded and explainable. As a decision-maker, you want to understand where data and results come from and the system must be strong and unbiased. New technologies and AI systems must be developed and implemented in a deeply meaningful way and learn from and collaborate with your recruiters and employees. The systems support and provide objective new data, with which people can ultimately make faster and better decisions. In this way, you can grow and learn from employee, applicant, and recruitment data and you implement a consistent process that complements and supports recruiters. This allows you to monitor over time and grow in the direction you want to go with your organization.<\/span><\/p>

The Dutch version of this article by co-founder Felix Hermsen can be found <\/span>here<\/span><\/a>.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n Contact us for more information\n<\/button><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tContact us for more information<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Computers can process data better, faster, and more accurately than humans and therefore help in the recruitment process. But that will only yield good results when systems are transparent, free of prejudice, and people-friendly. How does human-centered AI work in practice? Systems usually analyze data better and faster than humans and sometimes also data that […]<\/p>\n","protected":false},"author":1,"featured_media":316,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[3],"tags":[],"class_list":["post-257","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-article"],"_links":{"self":[{"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/posts\/257","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/comments?post=257"}],"version-history":[{"count":15,"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/posts\/257\/revisions"}],"predecessor-version":[{"id":8703,"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/posts\/257\/revisions\/8703"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/media\/316"}],"wp:attachment":[{"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/media?parent=257"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/categories?post=257"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/neurolytics.ai\/en\/wp-json\/wp\/v2\/tags?post=257"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}