What is AI and machine learning?

Computers perform the functions typically performed by people through AI (artificial intelligence) technology. These systems improve with continued use over time, which is referred to as machine learning. In a recruiting context, some applications of machine learning include communication with search partners, processing job applications, and addressing candidate inquiries. Machine learning streamlines the hiring process by allocating time-consuming routine tasks (such as sorting through hundreds or thousands of CVs) to machines and freeing up recruiters’ time. This leaves businesses with more manpower to devote to other organizational functions. This is especially true for retail and other industries with high-volume recurring hiring needs. The integration of AI and ML in recruitment reduces the cost of hiring overall, as it decreases recruiters’ workload. 

AI may be beyond the scope of understanding for many recruiters and HR professionals. However, as these tools become more and more common in the field of recruiting, recruiters should aim to close this knowledge gap and develop a rudimentary understanding of the inner workings of AI technology and its subset, machine learning. This will ensure continued transparency and accountability throughout the hiring process. 

The role of AI and ML in recruitment in 2021

The pandemic has accelerated digital transformation across all industries. 24% of businesses have implemented AI for their hiring processes and 56% of managers plan to integrate this technology in 2021. In order to assess an employee’s culture fit into the company, recruiters can use re-hiring assessments and virtual interviews in conjunction with AI tools. The use of AI and ML in recruitment will become standard practice in the long term and is already well on its way to becoming a norm across industries. Despite the limitations of the pandemic, the prevalence of digital hiring technology and AI has given employers unprecedented access to a global talent pool. Skill set and compatibility will be more considerable factors than physical location when it comes to the job search. AI and machine learning allows remote workers to access opportunities from anywhere in the world. 

Chatbots and conversational AI 

One common application of AI is through chatbots, or conversational AI. Conversational AI uses Natural Language Processing, or NLP, a technology that allows computers to comprehend and process human language. Conversational AI uses NLP to process both written and spoken language. Voice-activated systems, such as the Amazon Alexa or Siri for iPhone, have applications in an enterprise context, such as voice-driven or automated customer service messaging. The more common application for recruiters are text driven systems, including conversational chatbots. Machine learning allows for this processing capability to improve over time; continual improvement is intrinsic to machine learning. Therefore, although it may initially seem that this communication method lacks a human element, over time the software’s language processing capability becomes more and more natural. Conversational AI will continue to improve over time through machine learning and will ultimately have a positive impact on candidate experience.

Impact on candidate experience

The integration of AI and machine learning in recruitment has positive impacts on candidate experience. Automating parts of the recruitment process allows employers to avoid common pain points for candidates, such as ghosting (not informing a rejected candidate of their status with their application). AI recruitment tools also offer flexibility in scheduling. Candidates are able to complete steps in the recruitment process on their own time. Accordingly, this broadens the potential applicant pool for a position by removing scheduling conflicts as a barrier to application. This flexibility in scheduling also gives employers broader access to a global talent pool, as time zone differences would be removed as a barrier to application as well. 

Further, the integration of a conversational AI into a mobile-friendly format provides an intuitive avenue for communication that feels less confrontational than the standard interview process. According to the Pew Research Center, 95% of post-secondary graduates in advanced economies own a cellphone. Texting is the preferred form of communication for Generation Z. Studies demonstrate that open rates for text messages amongst Generation Z are 90%, compared to 18% for email. Adapting to the preferred communication style of the workforce will be a key strategic consideration in 2021 and beyond. 

A level playing field?

The use of AI and ML in recruitment provides a standardized process which should level the playing field for all applicants. Each applicant goes through the same vetting process and is evaluated using objective and relevant metrics. This is significant because the integration of machines and software into recruiting removes personal identifiers from the process. Some personal identifiers, such as names, are often a grounds for discrimination. A Harvard Business School study found that 25% of Black Americans that changed their names on their applications to sound more “White” received a followup from the employer, compared to just 10% when they used their real names. In this regard, AI has the capability to make some parts of the onboarding process less biased. 

While AI technology eliminates the “knee-jerk” unconscious bias that may influence hiring outcomes, AI is only as unbiased as the people who design it and the data it draws from. To illustrate, a company may try to create an “ideal candidate” profile based on data about its existing workforce. If this workforce is predominantly skewed towards one demographic, such as men or White individuals, this may hinder opportunities for women or people of colour. This is because they would not fit the ideal candidate profile generated by the AI tool. Users of AI and machine learning technology for recruitment must be weary of the tools’ ability to unintentionally perpetuate bias. There must be a concerted effort to ensure that the software provides an equitable experience at each stage of the process. This is ultimately in employers’ best interest, as diversity in the workforce has been linked to positive performance and profitability outcomes. 

The human element

AI and machine learning have allowed for the recruitment process to be more streamlined and efficient than ever before. Employers now have unprecedented access to a global talent pool, while candidates’ application opportunities are not restricted by their location or schedules. The inner workings of AI software remain obscure to many of its users despite its widespread use in the field of HR and recruitment. Users of AI tools should develop an understanding of how they work and seek to improve them. They should seek to design and integrate unbiased systems. When it comes to making impactful decisions about employment, human input will always be required and should be used in conjunction with AI technology.