Although it may be daunting to think about automation, AI, and machine learning in work and jobs, it is reassuring that people will always need to assist and steer technology.
Furthermore, a robot can never replicate the complexities and nuances of human interaction. This is excellent news and a source of relief for HR professionals around the globe.
Nevertheless, the adoption of AI and Machine Learning is expanding, aiding recruitment and HR teams in enhancing efficiencies and cutting down on manual tasks. Here is the way.
Rise of AI in HR
AI and ML, two intertwined technologies, can revolutionize the HR industry. Several companies have begun integrating artificial intelligence into their HR departments.
The study ‘Impact of Technology in 2022 and Beyond,’ carried out by the Institute of Electrical and Electronics Engineers (IEEE), points out AI and Machine Learning as the leading technologies in 2022. 51% of the surveyed individuals also mentioned that they plan to incorporate AI and Machine Learning as primary technologies within the next one to five years.
The success of a business relies on the collaboration between individuals, procedures, and technology to ensure optimal performance. The HR industry integrates AI and ML to automate many time-consuming administrative tasks.
In a study by Tidio, 95% of the 1068 hiring managers and HR professionals thought that AI would be advantageous for the application process. Around 68% of the individuals involved in the research believe that AI will help reduce or eliminate unintentional bias in the recruitment process.
What are the potential applications of Machine Learning in the field of HR?
HR professionals comprehend that managing a significant amount of data is necessary when pre-screening, recruiting, and onboarding new employees. As a result, delays can occur in the recruitment process.
This is particularly accurate when a high volume of applicants or job openings are there. Machine Learning can streamline candidate-employer communications, provide instant hiring process notifications, and track documents efficiently.
Information gathered from both existing staff and potential job candidates has the potential to train artificial intelligence for a wide range of functions in the realm of human resources. Responsibilities involve finding candidates, evaluating employee satisfaction and commitment, and forecasting attrition risk.
Multiple companies, like Unilever, utilize Machine Learning for talent acquisition. AI can be used to search social media sites such as LinkedIn to find resumes or profiles that align with the system’s predefined data sets.
This type of training can assist the algorithm in identifying profiles and resumes of ideal job candidates. Machine Learning can be used for sorting job applicants’ resumes, provided that the data utilized is unbiased.
Chatbots can substitute the first interaction between a recruiter and a potential employee. Chatbots can inquire about skills, past roles, salary expectations, and more from the candidates. It can then assess the feedback, categorizing the interviews into high-priority second interviews and those that can be eliminated.
Chatbots can also serve as a valuable asset within a company. Possible applications involve streamlining usual HR inquiries by either answering them directly or forwarding them to the appropriate HR specialist.
Can machine learning for HR go wrong?
In 2017, Amazon ended its development of AMZN.O, an AI recruitment system it had been developing for five years. As per multiple sources, such as the American Civil Liberties Union (ACLU), the system failed to prevent discrimination against women in the hiring process. The bias occurred due to the data that was utilized to train the system.
Amazon utilized resumes from the past 10 years during the job application process at Amazon. The concept was for the AI to use the collected data to recognize the best candidates when resumes were received. This would mean that only humans would have control over the ultimate hiring choices.
The AI training did not consider people who are looking for tech industry jobs. Most of the tech industry in the US is controlled by men. Zippia reports that in 2022, 73.3% of the US tech industry comprises men, leaving slightly more than a quarter of roles held by women. Due to the imbalance in the data favoring males, the AI unintentionally learned to undervalue characteristics associated with females. It excessively emphasized factors linked to males.
Resumes containing the term “women’s” were rated lower by the AI. It showed bias against individuals who were educated at colleges exclusively for women. It also showed a preference for terms like “captured” and “executed”, which seemed to be commonly employed by male engineers.
Despite sounding like a horror story, AMZN.O shouldn’t deter anyone from considering AI in HR. It serves as a great demonstration of how data can effectively educate machines. AMZN.O had the potential to be a groundbreaking tool with accurate and impartial data.
5 Applications of Machine Learning in HR
The HR industry is seeing advancements in machine learning, as businesses use the technology to improve outcomes and streamline operations. Let’s explore how ML is changing the landscape of HR operations.
1. Selecting the top talent
Companies use machine learning methods to improve their capacity to find competent applicants. Platforms such as Indeed, Glassdoor, and LinkedIn effectively use machine learning to find qualified candidates and expedite searches by utilizing state-of-the-art intelligent algorithms.
HR chatbots actively interact with candidates, gathering candidate information and asking basic screening questions. Recruiters are then presented with the results of an applicant assessment by machine learning, which uses this information.
2. Reducing prejudice in recruiting selections
While human involvement is still necessary when using AI, machine learning in HR offers accurate and insightful data that improves hiring effectiveness. Especially, it is essential for reducing human prejudices that could prevent your company from choosing the best candidates.
Hiring managers find the candidate screening process made easier by machine learning, which hides personally identifiable information prone to bias, including last names, places of residence, family histories, etc. It also helps in identifying objective job descriptions that draw in a wide range of qualified applicants. By removing manual biases, ML systems further assure fair compensation for hiring at the same level.
3. Planning and workforce optimization
Enterprises need to prioritize efficient workforce planning to meet their goals. AI and ML algorithms can enhance scheduling and resource allocation by considering skill sets, workload distribution, business requirements, and staff availability. This strategy can result in more productive work, better staffing, and more economical utilization of human resources.
4. Strengthening inclusion and diversity
Diversity and inclusion are important to many firms, and machine learning algorithms help HR staff spot biases and prejudice in hiring, performance reviews, and promotion procedures. For example, these algorithms can identify language that might inadvertently propagate bias or prejudice against particular demographic groups based on age, skin color, or other attributes.
5. Boosting workplace morale
HR uses machine learning (ML) to analyze several employee data signals to provide more individualized employee communications. The HR system uses machine learning (ML) to process hundreds of unique data points about employee engagement across the enterprise. This task could take human workers days or weeks to complete.
These algorithms compile data from various sources, such as surveys, HRIS systems, and more, to identify the variables that affect employee engagement, including workload, satisfaction, salary, relationships with managers, and vacation time. Predictive analytics and real-time monitoring are used in machine learning to find trends that lead to staff attrition.
Conclusion
Fears about generative AI in HR are unjustified because dealing with people and maintaining an organization’s smooth functioning will always require a human touch. AI and ML will significantly alter HR professionals carry out their daily duties.
HR practitioners may leverage AI and machine learning as extremely effective tools to optimize and streamline HR workflow. ML may be applied in many different contexts, ranging from large-scale sentiment and performance analytics to chatbots to automate basic HR chores.
The caliber and quantity of data utilized to train the machine learning model determines how effective the technology is. Machine learning can benefit an HR department if it is made sure that the data is impartial and representative.
