The digital transformation will create new professions in the next ten years: AI auditors, ethical data managers or human-machine teaming managers will be part of the HR team in the future. Because of artificial intelligence (AI), the extensive use of applicant and employee data and the automation processes lead to new challenges. But these can only be solved with particular IT expertise or new IT colleagues.
In addition to the many pluses that digitization brings with it, conflicts between people and technology arise at the same time. It can therefore be assumed that HR managers will also take care of the cooperation between employees and machines, algorithms that conform to values and future ethical use of data. Since IT knowledge is not one of their core competencies, other experts are needed: external specialists take over the tasks, AI auditors, ethical data managers and human-machine teaming managers are hired.
AI Auditors Prevent Wrong Decisions And Prejudices
AI is already being used in application processes today. The AI software can not only tailor job advertisements to the desired target group and place them appropriately, but it also reads relevant information from applicants’ CVs, matches it with the job advertisement and thus makes an initial preselection for the recruiter. If the information for evaluating the candidates is missing, HR managers can use chatbots with AI that specifically ask them. Moreover, the chatbot can give potential candidates standard data about the organization, the work promotion and the application cycle whenever required.
The bot helps closely involved individuals and can likewise be utilized for internal correspondence. Then, at that point, he addresses representatives’ regular inquiries concerning HR issues like advantages, excursions or days off. This saves the HR team a lot of time. Because AI takes over automated processes and administrative tasks, this allows HR professionals to focus more on other essential tasks. But the use of AI also harbors dangers: The technology can make wrong decisions, sort out suitable talents in the recruiting process or communicate in its chatbot function that is inappropriate to values and brands.
An AI is only as good as the person programming and training it. The learning process of AI is based solely on data provided by the software developer. If data only illuminates a partial aspect of the tasks, then the system learns the same biased thinking as humans. For example, an AI chatbot could discriminate against applicants and employees if cultural and language differences are not considered. In addition, AI data always refers to the past. For example, if certain positions have never been filled by women or people with disabilities, AI may inhibit diversity in the company.
Or on the other hand, assuming an organization takes care of the simulated intelligence with idealistic prerequisites, it would be no big surprise that many competitors are consequently figured out. The algorithm bias auditors have an overview of any AI technology used in HR. They methodically examine all algorithms and monitor every element of the AI to ensure that it represents the company’s employer branding values at all touchpoints with applicants and employees and assists from an ethical point of view.
They are there to prevent misjudgments and uncover prejudices in communication and decision-making. AI auditors also ensure that large and diverse data sets are used when training the technology, which they constantly check. When using AI chatbots, they work closely with scientists of ethnography (a research method to get as close as possible to (every day) cultures) to understand applicants and employees in their diverse situations and with all their problems down to the smallest detail. This is the only way chat communication can lead to a positive experience.
Specialists Develop Ethical Data Culture
More and more applicant and employee data are being stored and used in human resource management. Digital recruiting and the digital personnel file are already a reality today. Cyberbullying and leaks can cause a lot of damage to the company, applicants, and employees. In addition, decisions about candidates and talent, their hiring, training and promotion increasingly depend on data.
Therefore, HR managers must maintain sight of the data topic related to safety and ethical responsibility. Because applicants and employees trust that their personal information will be handled fairly and in compliance with data protection regulations so that this leap of faith is not disappointing, companies need the function of an ethical data manager. In addition to protection and security, they primarily ensure anonymous, honest, transparent and equal use of data.
Managers Optimize Collaboration Between Robots And Humans
The cooperation between man and machine is also becoming the focus of HR managers since almost all work areas are digitized and automated. Teams of people and machines will prevail in all companies. For this cooperation to be practical, it should be consciously controlled. It is essential to bring together the essential skills of humans and machines in a goal-oriented manner. To do this, employees should first break down their prejudices and fears of robots or AI software.
The next step is to get to know and assess technical strengths such as accuracy, endurance, calculations or speed. In the end, they should be confronted with the human abilities of creativity, differentiated perception, judgment, A new way of working and thinking is needed to shape this cooperation optimally. Therefore, the Human Machine Teaming Manager’s central task is developing an interaction system through which humans and machines mutually communicate their abilities, goals and intentions. They also help to design efficient task planning for each affected work process and to form hybrid teams successfully.
They identify processes that newly available technologies can improve. Digitization is taking its toll: HR departments should already be building up specialist IT skills for everything to do with automation processes, AI, and sensitive data management – whether through further training for their employees or external consultants. Part-time or full-time jobs can be created for this in the medium or long term. Because the functions of the algorithm bias auditor, the ethical data and human-machine teaming manager will be needed in the future.