Recently, I’ve had the honor of coaching and mentoring some competent professionals as they sought career changes. The common denominator with these high-potential applicants is that their opportunities have been limited because of the arbitrary algorithms found in bots and the way keywords are highlighted. Bots and keywords have become the primary selection tool in Applicant Tracking Systems (ATS.) Sadly, these bots and keywords contribute to the lack of diversity in executive ranks. I understand that search companies see them to streamline the process for them and their clients by automating the initial screening of resumes. That is where the benefit ends. These bots and keywords often perpetuate biases and limit candidates’ diversity. Here’s how bots and keywords adversely impact diversity in executive hiring:

• Unconscious Bias Infiltrates and Dominates the Process: When ATS filters a resume, it looks for specific keywords. These keywords are often based on historical hiring data, industry standards, or extracted from the job description. However, if the keywords predominantly reflect a particular demographic or background, it perpetuates existing biases. For example, candidates from a non-traditional background are disadvantaged if certain educational institutions or experiences are favored.
• Contextual Understanding is Lost: Bots and keyword-based systems lack the nuanced understanding of a candidate’s experience, skills, and potential that a human recruiter can screen for. This leads to overlooking qualified candidates with unconventional but valuable experiences or skills that don’t match the predefined set of keywords.
• There is an Overemphasis on Educational Credentials: Some bots heavily weigh educational credentials when screening resumes. While education is important, overemphasizing specific degrees or institutions can exclude candidates with equivalent or superior skills gained through alternative paths or practical experience. This bias can disproportionately affect individuals from underrepresented or non-traditional backgrounds. All hiring executives must ask, when does practical, on-the-job experience trump education? We all have known highly educated executives who knew a lot of theory but did not know how to apply theory to real life or had all the education but ultimately lacked the soft skills and EQ to be influential leaders.
• Historical Biases are Reinforced: Often, historical hiring data used to train these systems reflects a lack of diversity. The algorithms will likely learn and perpetuate those biases. This creates a cycle where the system continues to favor candidates like those hired in the past, further hindering diversity in executive ranks. This bias starts with the job description identifying the desired skills, experience, and education. These job descriptions often contain biases that are translated into the keywords the bot seeks.
• Language and Cultural Biases are Included: Bots struggle with understanding variations in language, dialect, and cultural expressions. Candidates with diverse linguistic or cultural backgrounds are often penalized if their resumes do not conform to the system’s pre-established language patterns, leading to exclusion from consideration.
• Transparency and Accountability are Lost: Many companies use third-party vendors or proprietary algorithms for their ATS, and the inner workings of these systems are often not transparent. This lack of transparency makes it difficult to identify and address biases within the algorithms. It is impossible to understand how these systems affect seeking candidates who are more diverse in thought, experience, culture, and education.
• Skill Assessments Become Limited and Less Flexible: Most bots may not evaluate a candidate’s potential or transferable skills effectively. Instead, they focus on specific job titles or industry-related terms. These biases disadvantage candidates seeking to transition from different sectors or roles, limiting the diversity of experiences in the candidate pool. An example of how using titles as a keyword is that there are genuine differences in the roles and responsibilities of titles from organization to organization and industry to industry. For example, a director in one company may be a C-suite executive in another.
• The Ability of a Candidate to Grow Into the Job is Lost: These keywords and bots often seek specific skills and experiences. This tendency eliminates candidates who are 80-90% fit, but the missing skills are quickly learned. The keywords and bots typically lose the ability to identify soft skills, leadership skills, and learning capabilities. These abilities define a great leader from an average leader and what could be a great hire.

Organizations should actively review and update their ATS, incorporating ethical AI practices to address these issues and promote diversity in executive hiring. This effort includes regular audits to identify and mitigate biases, revisiting the choice of keywords, providing additional context to algorithms, and ensuring transparency and accountability in the hiring process. In hiring your next leader, the advantages of hiring more diversity of thought, experience, and background are vital to creating an innovative and vibrant culture.