Landing a Job in the 21st Century: Automated Recruitment

Traditional hiring methods are no longer effective in attracting top talent. In order to build a top-notch team, new technologies such as gamification and AI have become crucial. Traditional interview questions often fall short when it comes to assessing a candidate's true potential; particularly when evaluating a candidate on multiple fronts such as experience, work ethic, and ability to perform under pressure. It is becoming increasingly difficult to retain a good candidate throughout the hiring cycle. A survey found that 70% of candidates considered leaving their last hiring process (Walters, 2022). It is therefore necessary to align the recruitment process with the requirements of the next generations. It is not feasible to establish an innovative company without the appropriate workforce. Employing the appropriate people for the right positions at the suitable time is crucial and demands solid resource management. A successful tactic to meet corporate hiring goals is gamification, that is the use of game components in otherwise unrelated contexts. Quizzes, puzzles, challenges, and other game-like elements are often used throughout the recruitment process (Joy, 2017). Companies that use gamification to recruit candidates have discovered that games can be used to increase candidate engagement. In addition, gamification provides crucial insights into candidates' personalities and skills that traditional recruitment processes cannot provide (Depura & Garg, 2012). However, the corporate sector is becoming increasingly fast-paced due to technological advances. In a world thriving on technology, the most successful companies are those that are open to change and therefore integrate such breakthroughs to automate their operations. Recently, adoption of artificial intelligence (AI) in hiring is a game-changer for the recruitment industry. This cutting-edge technology is loaded with a plethora of functionalities applying big data and predictive analysis to bring desirable disruptions to companies' recruitment processes (Jha et al., 2020). However, concerns arise when companies employ neurological games or emotion-sensing facial recognition as part of their assessments, claiming that they can obtain deeper insight into individuals' abilities or personalities.

Figure 1 - AI is revolutionizing the recruitment process (Ceipal, 2022).

Gamified Recruiting for Talent Acquisition

Gamification is an online marketing strategy used to increase engagement for a product or service. It involves applying typical game elements such as scoring, competing with others, and game rules to otherwise unrelated concepts. In other words, it means injecting some entertainment into processes that do not typically have a game context. It fosters user engagement and makes activities that are unpleasant or difficult more enjoyable. It also provides guidelines and procedures to guide problem-solving through the use of game principles. Gamification is a goal-oriented activity that fosters motivation, making it an excellent tool for learning (Alsawaier, 2018; Joy, 2017). A compelling illustration of the value of gamification in recruiting comes from Google’s now-famous mathematical riddle. Google anonymously published arithmetic equations on billboards in Harvard Square and Silicon Valley in 2004 which, when properly solved, led to a website with a different equation (Musacchio, 2011). If answered correctly, this would in turn allow participants to submit their curriculum vitae (CV). Google's reasoning was that those who had the skills and determination to achieve this riddle would be the ideal candidates. This would benefit both parties: Google would instantly pre-screen and attract top-notch candidates, while applicants would enjoy the game and potentially land a job.

Figure 2 - Google's genius billboard riddle to attract top-notch candidates (Igantova, 2013).

Despite recent media coverage, the concept of gamifying the hiring process is not new. In fact, the US Army pioneered the idea of gamifying the recruitment process in 1999. In 2002, 'America’s Army' was launched, an online game that allowed users to virtually tour the army at their leisure and assess whether soldier service met their needs, interests, and talents (Kapp, 2022). The online game aimed at attracting technologically savvy video game enthusiasts and by 2007, more than 7.24 million people had played the game (Britannica, 2011). Today, as companies scramble for high-quality talent, gamification has made it to the top of the global talent acquisition agenda. Since 1999, applications of gamification have grown tremendously, with games being nowadays used to assess applicants' risk tolerance and other characteristics that can influence how well they would perform in certain job positions. Despite projections that most gamification approaches would not succeed, the global gamification sector earned $9.9 billion in 2020 and is projected to reach $95.5 billion by 2030, according to Allied Market Research (Allied Market Research, 2021). While Google employed a public riddle to attract applicants, games can alternatively be online quizzes or challenges, like HackerRank’s (HackerRank, n.d.) and Codility's (Codility, n.d.) online programming exercises. For example, benchmark.games has developed simulation online games which are designed to match candidates with the role that suits them best based on behavior science (Benchmark Games, n.d.)

Figure 3 - 'America's Army' was a first-person shooter video game developed by the United States Army to inform, attract, and recruit potential soldiers. (Kapp, 2022).

Artificial Intelligence in the Hiring Process

Although it sounds like a fairly new concept, artificial intelligence (AI) emerged as a discipline of computer science in the 1950s (Asimov, 1950). AI is a collection of technologies aimed at enabling machines or systems to see, comprehend, act, and learn. Thus, this field strives to create computer systems capable of performing and automatizing activities traditionally linked to human intelligence, such as object and face recognition, driving, illness detection, and interpreting natural language, both spoken and written (Brown, 2021). The foundation for AI is laid by algorithms, which are defined as programmed processes that convert input data into results to help humans make decisions. In other words, algorithms are an ordered set of instructions, operations, or processes that make it possible to perform a specific task or find a solution when a problem arises (De Angelis, n.d.).

The rise of algorithm-driven human resources (HR) can be traced back to Google. They were among the first major companies to consider using algorithms to manage HR—dubbed “People Operations” by Google (Pavel, 2018). These algorithms collect information from interview questions, surveys, and candidates’ CVs, to name a few. Next, they process and screen a CV for keywords, accomplishments, and other details that show how a candidate meets the needs of the job. AI-powered technology allows companies to automate the hiring process, minimizing costs and time, while helping reduce unconscious bias in the hiring process. In digitally-transformed organizations, AI software also automates processes like calling prospects, selecting resumes, and replying to candidates via email (Vinay, 2022). Powered by organizations like Google, modern organizations use algorithms to identify trends in employment data, as well as matches and test scores, to select the best applicants. Noteworthy, the HR industry uses these algorithms to gather data from work environments that identify employee skill gaps to suggest improvements and help them advance their careers while increasing satisfaction. Though AI might aid in decision-making, it is important to highlight that human biases may influence the datasets and algorithms used to drive AI. (Chen, 2022; Hunkenschroer & Luetge, 2022). Although algorithmic bias has long been a concern, most companies believe that AI can contribute to a more diverse workforce.

Figure 4 - Artificial Intelligence, Applicant Tracking System and Machine Learning lay the foundation for contemporary automated recruitment (Tiwana, 2018).

Chatbots: The Ultimate Job Search Assistants

An HR chatbot is a virtual assistant that attempts to mimic how employees and applicants communicate in order to automate complex tasks like shortlisting, interview scheduling, and managing candidates’ referrals. As a chat interface, the HR chatbot uses AI to scan and interpret conversational inputs and provide the user with the most appropriate responses (Chen, 2023). By asking candidates questions interactively via a chatbot, AI can boost cognitive engagement. The AI ​​algorithm examines and then compares the content of the candidates' answers with that of the database's top-performing employees (Sharma, 2018). Chatbots, which are able to respond to up to 80% of standard questions in minutes, are increasingly being used to deliver customized information to potential applicants and to guide them through the job search, application, or even onboarding process (Papas, 2018). The sophistication (and friendliness) of chatbots are rising and a few examples include Olivia from Paradox, Amy from Eightfold AI, and Mya from StepStone (Jones, 2022).

Figure 5 - Hi, I'm Olivia. The conversational AI assistant from Paradox which aids the world's largest organizations in the hiring process (Paradox, n.d.).

The majority of chatbots are capable of navigating employment sites, delivering suitable opportunities to site visitors and assisting with the application process. They can also arrange interviews, coordinate schedules, and send reminders to the appropriate parties (Suta et al., 2020). This is particularly important given that approximately half of all applicants will have a negative opinion of a company if they do not receive an answer within two weeks. Additionally, according to a Conference Board survey, only 7% of candidates will seek another job at the same company if they receive no response after an interview, while 18% may even take a negative action against the organization such as refraining to recommend it to others or writing a negative review (The Conference Board, 2023). To personalize and even persuade interactions with prospects, chatbots use real-time data and machine learning, a subset of AI. Machine learning is the ability of a system to capture and integrate information through large-scale observations, as well as to evolve and extend itself by learning new knowledge without being programmed with that information. That is, systems that use data to improve performance and learn things they weren't originally intended to (Woolf, 2009). Although chatbots are becoming more human as technology advances, 60% of job seekers still prefer to chat with a live customer service agent, according to UserLike. However, more than 70% of users can no longer discern whether they are talking to a chatbot or a person (Leah, 2022). Businesses are increasingly using AI-powered solutions to streamline hiring and 23% of customer service organizations are currently using AI, according to Salesforce. Additionally, IBM estimates that using chatbots can reduce customer service costs by up to 30% (Watters, 2022).

ATS: The Automatized Hiring Manager

Candidates spend hours preparing their CVs, tailoring them for each specific job, polishing their cover letters until they summon up the courage to press the submit button. Despite best efforts, most candidates do not get enough interviews, even for positions for which they are highly qualified. Why not? Most candidates are unaware that in the modern world, when a CV is submitted, it is usually routed to a computer or an AI-controlled software rather than a human. In fact, there's a good chance a real person will never see their CV. Companies are seeing record numbers of applicants and HR managers just cannot keep up. ATS is being employed by a rising number of companies to screen CVs (Heilweil, 2019). ATS is an AI-driven software program designed primarily to search CVs for specific keywords and exclude those that don't match the job description. At the forefront of screening, ATS enables companies to create shortlists of potential candidates by quickly screening hundreds, if not thousands, of applications for a single job ad (Trivella, 2023; Workable, n.d.). In addition, ATS functions as a database for all recruiting processes and enables automatic monitoring, with candidates’ information being stored to form talent pools. For AI-powered firms, it is sometimes more challenging to convince qualified candidates to submit their applications than to identify them.

Figure 6 - ATS gathers and screens thousands of CVs automatically, rejecting the least qualified candidates. This short listing is based on important keywords and qualifications required for a particular position (Winzer, 2018).

AI software developers, such as Textio, employ AI in the form of text-mining algorithms (i.e., algorithms that extract useful information and insights from large amounts of unstructured text data) to forecast the attractiveness of a job posting based on the hiring rates of millions of other job posts. Textio, powered by data from half a billion job postings, helps write job ads to attract the most qualified and diverse talent pool for the positions a company needs to fill. Textio provides real-time feedback and analytics by flagging potentially biased language, identifying groups who may be negatively impacted by that language, and suggesting more appropriate terminology that appeals to a more diverse audience. (Hunkenschroer & Luetge, 2022; van Esch & Black, 2019). However, these AI-driven systems are built on machine learning, which demands massive quantities of data in order to discover patterns or categories. Thus, faults in the data or small datasets might skew the results. One such example is Amazon's AI-driven algorithm, which learned how to identify the top candidates by studying all the CVs submitted to the company during a ten-year period. Because this is a male-dominated sector, the great majority of applicants were men. As a result, the engine ranked applications with words, phrases, and affiliations generally associated with women as less favorable - since they occurred less frequently. Amazon abandoned its experimental AI hiring tool after finding it discriminated against women (Dastin, 2018).

Emotion Recognition in Hiring: From Science to Sci-Fi

Emotions are key to comprehending human interactions, especially those mediated through facial expressions. Emotion recognition is a combination of AI, machine learning, biometrics (physical traits or biological measures that can be used to identify individuals), and big data (large data sets that can be used to analyze human behavior and interactions). This AI branch seeks to detect emotions in human facial expressions by evaluating body language, tone of voice, heart rate, respiration, and eye movement (Kaur et al., 2022). Despite deep concerns, this industry has grown in popularity over the past decade and is expected to reach $85 billion by 2025 (Market Research Engine, 2021; Nash, 2021). Emotion recognition has been used for a variety of purposes, some of which could be argued to be ethically questionable. Emotion recognition is being heavily adopted by the recruiting industry as it allows for a thorough assessment of prospective employees, rating them on empathy or emotional intelligence, among other traits (Knight, 2021; Metz, 2020). During the COVID-19 pandemic emotional recognition has been employed by teachers in China to remotely monitor student participation while they complete coursework at school or at home ("Ai Reinventing the World", n.d.). The software also tracks how quickly students answer questions, records their performance, creates reports on their strengths, limitations and motivation levels, to then predict their grades (Andrejevic & Selwyn, 2020; Chan, 2020). Notably, it is also used for recognizing “dangerous individuals” and “high-risk” groups in China, whether they have a criminal record or not (Article 19, 2021). It has also been used to control the US-Mexico border (Kinchin, 2021). However, this was not without controversy. Even for humans, it can be challenging to attribute a specific emotion to a facial expression. Different people can recognize distinct emotions in the same facial expression. This is even harder for AI. Thus, the accuracy of existing emotion recognition technologies is still being questioned.

Figure 7 - In Hong Kong, an AI-driven system reads children's emotions as they learn (Chan, 2020).

Emotion recognition is highly subjective and involves a number of privacy, fairness, and ethical concerns. First, the science behind emotion analysis is controversial: while some defend the existence of basic and universal emotions, others contend that age, schooling, and cultural background can affect all emotions (De Souza, 2018; Jack, 2012). Second, emotion recognition systems available today still provide unacceptable levels of bias as certain physical expressions differ between cultures. Additional controversy emerges when corporations employ neurological games and interviews as part of their evaluations, stating that this will aid in portraying the ideal applicant. HireVue, a talent experience platform, is one such example. HireVue is a company intended to streamline the hiring process, using facial/emotion recognition technology and algorithms to conduct video-based and game-based pre-hire assessments of job applicants (HireVue Team, 2019). It captures tens of thousands of data points from each video interview and feeds them into algorithms that determine the employability of each job seeker. The technology analyzes candidates' facial movements, word usage, and spoken tone and compares them to other candidates based on an automatically generated employability score. HireVue's evaluations have grown so prevalent in some industries that colleges make particular efforts to instruct students on how to behave and talk to achieve the best outcomes (Burke, 2019). Despite the fact that the system has been employed by over 100 companies, including Hilton and Unilever, and that over a million job applicants have been studied (Harwell, 2019), HireVue's facial monitoring software has been dismissed due to flawed AI-driven algorithms (Khan, 2021).

Conclusions

The employment of AI seems to be a badge of honor for companies looking for new employees these days. From sourcing and screening candidates to interviewing and virtual onboarding, the use of AI in recruitment is undeniably changing HR’s capabilities. With the help of AI, companies can streamline and automate many of the tasks involved in recruiting and selection, making the process more efficient and quick. However, if only humans make mistakes, then AI is also human: flawed algorithms or scarce data sets feeding into algorithms can lead to biased software and technical artifacts. AI-driven software during the hiring process enables HR managers to gain better insights into candidates' skills and expertise, eliminating most recruitment challenges while enabling better data transparency. However, the recruitment process should not be fully automated as it is still important for HR to review CVs and conduct interviews to ensure the best candidates are selected. AI should therefore be used as a complementary technical sphere that enables recruiters to make data-driven and therefore more reliable decisions.

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Cover Image - RMS, N. (2019). 5 New Applications of AI in Recruitment. [image]. https://www.naukrirms.com/blog/5-new-applications-of-ai-in-recruitment/

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