How AI is Revolutionizing Talent Acquisition: Candidate Interviews (pt 3)
Updated: May 16
** This article is part 3 of a series we are doing on the use of AI in our Talent Acquisition.**
The use of AI in talent acquisition has revolutionized how organizations conduct interviews. As the demand for talent increases, the recruitment process has become more competitive, and companies are searching for innovative ways to identify the best candidates. AI-driven interviewing techniques have emerged as a promising alternative, but do they hold up to the hype?
AUTOMATED INTERVIEW SCHEDULING
In automated interview scheduling, candidates have access to the interviewer's calendar to choose an available time slot, eliminating the need for back-and-forth communication between the candidate and interviewer. As a result, it significantly reduces the scheduling time and effort of the hiring team, allowing them to focus on other critical tasks. It also improves the efficiency of the recruitment process, reduces the chances of scheduling errors, and increases candidate engagement.
According to a survey conducted by Lever, an ATS provider, 86% of companies surveyed use some form of automated interview scheduling. The survey also revealed that automated interview scheduling significantly reduces the time spent scheduling interviews and improves the overall candidate experience. In addition, it found that candidates were twice as likely to complete the interview process with an automated scheduling process.
Another study by Jobvite revealed that 64% of candidates said that automated scheduling tools made the interview process more convenient, and 63% of recruiters said that it helped them to schedule interviews faster.
AUTOMATED/ ASYNCHRONOUS VIDEO INTERVIEWS (AVI)
One of the most common AI-driven interviewing techniques is the use of audio or video interviews (AVIs). An AVI is an interview conducted via a pre-recorded video response or a live video conversation between a candidate and an AI-powered system. These exchanges negate the need for both parties to be on the call simultaneously, allowing candidates and recruiters to participate on their own time. AVIs help to standardize the interviewing process, ensuring that all candidates are asked the same questions and are evaluated in the same manner.
The benefits of AVIs are numerous :
They allow recruiters to save time and money by eliminating the need for in-person interviews.
AVIs can be used to assess a large number of candidates quickly, allowing recruiters to focus on the most promising candidates.
AVIs can help to reduce bias in the interviewing process.
By asking all candidates the same questions in the same order, recruiters can reduce the influence of factors such as a candidate's appearance, accent, or gender on their evaluation.
According to a study by HireVue, AVIs can reduce time-to-hire by up to 90% and improve candidate diversity by up to 6x. Another study by Stepstone (formerly Mya) found that AVIs can reduce time-to-hire by up to 50% and improve candidate engagement by up to 300%.
Despite the benefits of AVIs, there are also drawbacks. One potential issue is the need for more human interaction. Interviews are a two-way process; candidates often want to ask questions and learn more about the company culture, team dynamics, and job responsibilities. AVIs do not provide this opportunity, and candidates may feel disconnected from the organization.
Additionally, some experts argue that AVIs can perpetuate bias. AVIs rely on natural language processing (NLP) algorithms to evaluate candidate responses. However, these algorithms may not be perfect and could misinterpret a candidate's responses, leading to incorrect evaluations. There is also the risk that the algorithms could be biased if they are trained on a biased data set.
Several AVI platforms have emerged in recent years, and the field is getting competitive. These platforms use natural language processing and machine learning algorithms to evaluate candidate responses, identify relevant keywords, and provide data-driven insights on candidate performance.
Current platforms use AI to evaluate non-verbal cues, tone of voice, vocabulary, and facial expressions, providing insights into candidate personality and communication skills.
NATURAL LANGUAGE PROCESSING (NLP)
Natural Language Processing (NLP) focuses on enabling machines to understand and process human language. In talent acquisition, NLP-powered tools are used to improve candidate evaluation during interviews. NLP in interviewing works by analyzing spoken or written language to identify patterns and understand the meaning behind what candidates are saying. This technology can then provide feedback to recruiters. By analyzing candidate responses, NLP-powered tools can identify traits such as confidence, enthusiasm, and leadership potential. Another benefit of NLP in interviewing is the reduction of interviewer bias. In theory, NLP-powered tools can help eliminate this bias by evaluating candidate responses based on their content and merit alone, removing any subjective factors from the evaluation process.
Statistics show that NLP is gaining popularity in the talent acquisition industry. According to a report by Gartner, 38% of organizations are now using AI-powered tools for recruitment and candidate assessment. Furthermore, a study by LinkedIn found that recruiters who use AI in the hiring process are twice as likely to find and engage with high-quality candidates.
But how does it work? Some NLP-powered systems analyze spoken conversations in real-time, using machine learning to identify patterns in candidate responses and provide feedback to recruiters. Others analyze written responses, assessing candidates' personality traits and communication styles to provide insights into their potential job performance. Finally, a few other systems analyze publicly available data on candidates, providing recruiters with insights into their personality and communication style to help them better understand how they may fit into a team.
BEST PRACTICES FOR AI-DRIVEN INTERVIEWING
According to a survey by Gartner, 96% of talent acquisition leaders believe that AI will be a mainstream tool in their recruiting process. Another study by IBM found that companies that use AI in their recruiting process saw a 50% reduction in time spent on administrative tasks and a 67% increase in candidate quality.
However, there are still concerns about the use of AI in recruiting. In a survey by LinkedIn, 67% of job seekers expressed concern about the bias in AI hiring tools. Additionally, a National Bureau of Economic Research study found that AI hiring tools can lead to unintentional discrimination against certain groups.
As with any new technology, it's crucial to establish best practices when implementing AI-driven interviewing into your recruitment process to ensure that their AI interviewing tools are being used ethically and fairly. Here are some tips to consider:
Transparency and communication: It's essential to communicate with candidates about the use of AI technology in the interview process. Ensure that candidates are aware of how the technology is being used, what data is being collected, and how it's being used to evaluate their candidacy. Being transparent lets you build trust with candidates and ensure they feel comfortable with the process.
Representative data sets: AI systems are only as good as the data they're trained on. To minimize bias, it's critical to ensure that your data sets are diverse and representative of the candidate pool you're targeting. A study by Harvard Business Review found that AI can perpetuate existing biases in the recruiting process, so it's crucial to use diverse data sets to minimize bias.
Legal and ethical considerations: As with any recruiting tool, it's essential to ensure that the use of AI interviewing is legal and ethical. Ensure that you comply with all relevant laws and regulations, including data privacy laws. Additionally, ensure that your AI interviewing tools are being used ethically and fairly.
Integrating AI interviewing into your recruitment strategy: When implementing AI interviewing into your recruitment process, it's essential to consider how it fits into your overall recruitment strategy. Determine where it fits in the hiring process, which positions it's best suited for, and how it can be used to augment your existing recruitment practices.
CHALLENGES AND LIMITATIONS OF AI INTERVIEWING
AI interviewing offers many benefits, but it is not without its challenges and limitations. One significant concern is the risk of bias and discrimination in AI algorithms. As AI systems are trained on historical data, they may learn and perpetuate biases that exist in the data. For example, if an AI system is trained on a dataset of mostly male candidates, it may not accurately evaluate female candidates. In a 2018 study, researchers found that facial recognition software was significantly less accurate at identifying darker-skinned faces and female faces than lighter-skinned faces and male faces, highlighting the issue of bias in AI algorithms.
Another limitation of AI in interviewing is its inability to assess complex human traits, such as emotional intelligence and cultural fit. While AI can evaluate a candidate's skills and qualifications, it may struggle to evaluate softer skills that are crucial for success in certain roles. This is especially true for roles that require empathy, creativity, and interpersonal skills. In a survey by PWC, only 10% of executives said that they were using AI in evaluating a candidate's fit with company culture.
Moreover, AI interviewing tools are not immune to technological failures or misuse. For example, in 2020, a facial recognition tool used by a UK university to remotely monitor students during exams incorrectly flagged a black student as cheating. Such instances raise questions about the reliability and accuracy of AI interviewing tools and their potential for misuse.
It is crucial to prioritize the ethical and responsible use of AI interviewing tools to mitigate these risks and limitations. Employers should ensure that their AI systems are trained on diverse and representative datasets to minimize the risk of bias. Additionally, they should regularly evaluate and test their AI systems to identify and address any potential issues. Employers should also supplement AI interviewing with other evaluation methods, such as in-person interviews, to ensure a more holistic evaluation of candidates.
In the coming week, we will cover other ways that AI can assist with negotiating job offers.