The Human Element: Why AI Assessment of Behavioral Interviews May Fall Short

Sabrina Houssami

Assessment of job candidates has evolved over the years, and with the advent of artificial intelligence (AI), there has been a growing trend of using automated tools to evaluate potential hires. However when it comes to assessing behavioral interviews where candidates share stories about their past experiences the human element cannot be discounted. Avanti Search founder and CEO, Sabrina Houssami, explains further.

"When someone tells a story about their past which is exactly what a behavioral interview is asking them to do their facial cues, their tone of voice, their pace of speaking, and the way they engage with the person they're speaking to, are as much a part of their response, as the actual words within that answer. And often, we'll take cues from each other as to when to speed up, when to slow down, and what the interviewer needs to hear more of versus less of. Am I over-delivering on content right now, versus under-delivering? And so, the content itself can change dramatically based on being physically in person with the interviewer."

The crux of it is this: human interaction during an interview is dynamic and responsive. A skilled interviewer can adjust their approach based on the candidate's responses, providing clarifications or probing for more information as needed. Candidates can gauge the interviewer's reactions and adapt their responses accordingly. This interactive process allows for a mutual exchange of information and helps both parties in understanding each other better. However, with AI assessment, this dynamic interaction is lost, as the tool follows a predetermined set of rules and algorithms, limiting the flexibility and adaptability that human-to-human interviews offer.

Further, the way candidates tell their stories, the emotions they express, and the non-verbal cues they exhibit provide valuable insights into their communication skills, problem-solving abilities, and interpersonal competencies. Facial cues, such as a subtle smile or a furrowed brow, can convey meaning that goes beyond the words being spoken. These social mechanisms are essential for many roles, including those that require strong communication, teamwork, and leadership skills. Yet one of the limitations of AI assessment of behavioral interviews is that it lacks the ability to capture this full spectrum of human expression.

"You also have to consider that the biggest indicator of a candidate's capability to do well in a job is behavioral interviewing methods," says Sabrina. "And so it's a huge concern when an AI process – let's say around screening keywords in a CV drastically reduces the amount of candidates that are reaching the behavioral interview stage."

The use of AI tools to screen candidates based on certain keywords or phrases may result in qualified candidates being overlooked if they do not use the "right" words that are programmed into the tool. This can lead to a biased and incomplete assessment of a candidate's capabilities, as their true potential may not be accurately reflected in their responses, Sabrina explains. This not only does a disservice to candidates but also undermines the overall effectiveness of the assessment process.

Sabrina also highlights the fact that reducing a candidate's story to mere "sexy yes-words" is not only unethical but also oversimplifies the complex nature of human vocabulary. Each candidate is unique, and their stories should be evaluated in a holistic manner, considering the context, content, and delivery of their responses. AI assessment that focuses solely on keywords or phrases may miss out on the richness of a candidate's story and fail to capture the essence of their capabilities.

In conclusion, while AI can streamline the recruitment process, it may fall short in accurately assessing behavioral interviews due to the importance of non-verbal cues, tone of voice, and contextual understanding. Relying solely on AI assessment for evaluating behavioral interviews may result in inaccurate and incomplete assessments, ultimately leading to poor hiring decisions.

Organizations should strive for a balanced approach that combines the advantages of AI with human judgment, ensuring that AI tools are developed and tested for fairness and accuracy. Human interviewers should be trained to enhance their interviewing skills, focusing on the holistic evaluation of candidates. Ultimately, ethical considerations, fairness, and accuracy should guide the use of AI in the hiring process to make informed and fair hiring decisions.

To learn more from Sabrina, or to work with her on building your dream team, connect with her on LinkedIn.

Related topics : Artificial intelligence