Is Ageism in Hiring Prevalent?
A recent study by the National Bureau of Economic Research study exposed notable differences in job offer rates tied to the timing of age disclosure. When age remained undisclosed during the initial interview, younger and older candidates stood an equal chance of receiving employment offers! According to AARP, one in five adults over the age of 40 (21%) reported experiencing discrimination in the workplace. Age bias is often subtle and is difficult to identify. This blog will delve into the intricacies of age bias, explore its origins, and propose strategic approaches to mitigate its impact on job searches.
WHAT ARE YOUR AGE DISCRIMINATION PROTECTIONS?
The Age Discrimination in Employment Act (ADEA) is a federal law that prohibits employment discrimination against individuals over 40. The ADEA covers all aspects of employment, including hiring, firing, promotions, compensation, benefits, and training.
Key provisions of the ADEA
It is illegal for employers to discriminate against employees or job applicants because of their age in any aspect of employment.
Employers must have a legitimate reason for any employment decision that has a disproportionate adverse impact on older workers.
Certain actions are allowed if necessary to comply with another law or to conduct a bona fide occupational qualification (BFOQ). A BFOQ is a requirement based on a legitimate age-agnostic business purpose. For example, lifting 50 pounds as a warehouse worker may be a BFOQ. A 65-year-old worker able to lift this weight would be as qualified as a 25-year-old.
The ADEA is enforced by the Equal Employment Opportunity Commission (EEOC). The EEOC investigates age discrimination complaints and takes action against employers who violate the law.
Examples of Prohibited Behavior
Refusing to hire an older applicant if perceived as too old for the job.
Laying off older workers while retaining younger workers with less experience or qualifications.
Paying older workers less than younger workers for the same job.
Promoting younger workers over older workers who are equally or more qualified.
Refusing to provide training or advancement opportunities to older workers.
EEOC Statistics in 2022
Age discrimination in hiring is the second most common type of employment discrimination after sex discrimination.
The EEOC received 14,183 age discrimination charges in hiring.
The median settlement amount for these cases was $46,698.
EXAMPLE CASES OF AGE BIAS IN HIRING
EEOC v. UnitedHealth Group, Inc. (UHG) (2023) – UHG discriminated against older job applicants for the hiring of customer service representative positions. UHG used a hiring algorithm that disproportionately screened out older applicants, and the company had failed to take steps to validate its hiring algorithm. The lawsuit settled for $2.5M awarded to affected applicants.
EEOC v. IBM Corporation (2023) – IBM discriminated against older job applicants for software engineer positions. The EEOC investigation found that IBM had used an unvalidated hiring algorithm that disproportionately screened out older applicants. The company settled the lawsuit by paying $4M to affected applicants and modified its hiring practices.
EEOC v. Accenture LLP (2023) – Accenture discriminated against older job applicants for consulting positions using an unvalidated hiring algorithm. Affected applicants received $3.5 million, and hiring practices were required to be modified.
WHAT IS A HIRING ALGORITHM?
A hiring algorithm is a data-driven recruitment approach that systematically assesses applicants based on predefined criteria. It processes extensive data to predict job performance through pattern identification, aiming for efficient and consistent candidate evaluation. Each company may have its unique algorithm, contributing to variations in evaluation processes. Smaller companies may apply their algorithm manually, while large companies may use AI to enhance predictive capabilities.
HOW IS THE HIRING ALGORITHM APPLIED?
Hiring algorithms vary by company and are applied at various stages of the recruitment process. The information used to train and apply hiring algorithms is typically compiled and stored in a centralized repository, such as an Applicant Tracking System (ATS) or a separate database.
Data Collection and Preparation - The algorithm is trained using a dataset of past job applications, which includes information about applicant qualifications, experience, and performance. This data is cleaned and preprocessed to ensure its accuracy and consistency.
Algorithm Training - The algorithm is trained on the prepared data to identify patterns and correlations between applicant characteristics and job performance. This training process involves adjusting the algorithm parameters to optimize its predictive accuracy.
Resume and Application Screening - When a new job application is received, the algorithm scans the resume and application to extract relevant information, such as skills, experience, and educational background. This extracted information is then fed into the algorithm to generate a score or ranking for the applicant.
Interview Analysis - In some cases, hiring algorithms analyze interview transcripts or video recordings to assess applicants' communication skills, problem-solving abilities, and overall fit with the company culture.
Hiring Decisions - Hiring algorithms provide valuable candidate insights but should not be used solely for hiring decisions. Human review and oversight are crucial to ensure fairness, equity, and adherence to anti-discrimination laws.
For example, Microsoft might require a software developer to take a coding test, but Google may not.
A Sample Hiring Algorithm
Information from the resume, LinkedIn profile, and social media profiles are used to extract education, degrees earned, work experience, skills, recommendations, endorsements, and professional groups. Social media accounts provide data on candidate online presence, interests, and potential red flags.
Numerical scores are assigned to each piece of information based on its perceived relevance to the job requirements. (ie Working at Google scores higher than Salesforce)
A total score for each applicant by summing their scores.
Output a list of applicants ranked by their total score, with the highest-scoring applicants at the top.
HOW DOES AN ALGORITHM EVALUATE APPLICANTS FAIRLY?
Here are considerations to make sure algorithm results are bias-free:
Data Sources - The data used to train and evaluate hiring algorithms should be non-biased and representative of a diverse pool of applicants.
Data Predictability – Data used for decision-making is not subjective and is a reliable predictor for the job. Example - Using a rating of 1 to 5 on attractiveness for the algorithm is NOT OK!
Human Oversight - Human decision-making is integral to the hiring process. Hiring decisions align with fairness, equity, legal compliance, and other factors such as cultural fit and soft skills.
Continuous Monitoring and Improvement - A company's hiring algorithms should be monitored, evaluated, and adjusted to ensure consistent and unbiased results.
Most companies are actively committed to fair and equitable hiring of qualified candidates. By combining the strengths of validated hiring algorithms and inserting human judgment, companies can strive to make more informed and defensible hiring decisions.
Can A Single Person be Subject to Age Discrimination in Hiring?
If two candidates have equally rated experience against an employer algorithm AND interviewer input, either choice (over or under 40 years old) is valid. Qualified candidates do not have insight into the exact reason for failing to receive a job offer. As a result, cases of individual age discrimination occur but are difficult to prove. Following are examples of inappropriate interview behavior:
The interviewer made age-related comments during the interview or the hiring process.
The interviewer asked specific age-related questions related to job requirements.
The company's reason for not hiring the applicant is not credible.
The interviewer dismisses the candidate's ability to meet the physical requirements of the job.
CAN A HIRING ALGORITHM BE BIASED?
Accenture was found liable for age discrimination in hiring consultants by the EEOC in 2023 for the following reasons:
Lack of transparency - Accenture did not disclose the details of its hiring algorithm to the EEOC, making it difficult for the EEOC to assess whether the algorithm was fair and unbiased.
Use of subjective criteria - The algorithm used subjective criteria, such as cultural fit and teamwork skills, which are more difficult to assess objectively and can be prone to unconscious biases.
Failure to validate the algorithm - Accenture failed to conduct a validation of its algorithm to ensure that it was not discriminating against older applicants.
Lack of human oversight - Accenture relied heavily on the algorithm to make hiring decisions without adequate human oversight to identify and address potential biases.
This sample case highlights the importance of designing and implementing fair, unbiased, and transparent hiring algorithms. When job searching, applicants should ask about the hiring process and the factors the potential employer uses to make their employment decisions. Overreliance on automated decision-making and unrelated data requests could be potential red flags.
SIGNS OF AGE DISCRIMINATION AND AGE BIAS
Most companies do not publicly share their hiring algorithm. Following are descriptions of how to spot age bias.
Job Posting
Age Requirements - It is illegal to state an age requirement in a job posting unless the requirement is a BFOQ.
Keywords - Review postings for keywords or phrases that might suggest a preference for younger candidates, such as recent graduates or energetic. The overuse of these words can discourage older workers from applying for jobs.
Emphasis on recent graduate positions – Consistent, subtle use of entry-level language suggesting younger applicants can be a warning sign.
Overemphasis on Technologies – Every candidate should be up-to-date per the job description. Watch out for inappropriate emphasis based on job duties.
Narrow Salary Range - Although not illegal, stating a narrow or low salary range can discourage older applicants who may have higher salary expectations.
Network – Reach out to workers from the target company to assess attitudes, treatment, inclusion of older workers, or a predominance of a specific age group of employees.
Application Process
Asking for a Date of Birth – It is not illegal to ask for a date of birth on an application but the information cannot be used as a basis for hiring decisions. About 50% of companies request this information.
Requesting Graduation Year – It is not illegal to request a graduation date during on the job application but it cannot be used for hiring decisions. Try not to provide a graduation year unless required for onboarding paperwork. Companies may do background checks if a bachelor's degree is required.
Inquiries about Family or Marital Status - Asking about an applicant's family or marital status is illegal.
Requiring Social Media Profiles - Requiring applicants to provide their social media profiles can lead to age discrimination if the employer uses the information to assess their age or lifestyle choices.
Company Culture – Many companies have websites, social media, and news articles that give a sense of culture and values. Look for specific information about the company's diversity and inclusion initiatives. Search for past ADEA violations before applying.
Interview Process
Age-Related Comments - Making comments about an applicant's age, such as saying they look too old or young for the job, is illegal. Be prepared for those that hint about age or experience. Have answers ready that highlight your skills and qualifications and deemphasize your age.
Stereotypical Assumptions - Assuming that older applicants are less tech-savvy or less adaptable to change is discriminatory.
Inquiries about Retirement Plans - Asking about an applicant's retirement plans is illegal.
Physical Agility Tests - Requiring physical agility tests that are not job-related can disproportionately disadvantage older applicants.
Hiring Decisions
Favoritism Towards Younger Candidates - Selecting younger candidates over equally or more qualified older candidates can be discriminatory.
Lack of Transparency - Not receiving clear reasons for hiring decisions makes it challenging for older applicants to eliminate the possibility of age bias, particularly when younger candidates are selected.
Retention of Younger Employees During Layoffs - Laying off older employees while retaining younger employees with less experience or qualifications can raise concerns about age discrimination.
CONCLUSION
Older workers searching for jobs can experience age-related bias in many subtle (and not so subtle!) ways. Following is a list of bias-combatting job search strategies:
Understand your legal protections under the ADEA.
Scrutinize job postings for biased language.
Research companies before interviews. Research and prepare for the vague age-related questions. Check out the Age Friendly Institute for a list of companies.
Modernize and remove age references on your resume and LinkedIn profile.
Focus on adaptability, willingness to learn, and accomplishments during interviews. Avoid referring to age-revealing situations
Do salary research for what you are worth as an EXPERIENCED candidate. The government site www.bls.gov is a great source of information.
Keep your technical skills up-to-date. Identify skill gaps and put an education plan together.
When armed with knowledge and increased awareness, older job seekers can effectively navigate the hiring landscape, present themselves as a valuable candidate, and enhance their chances for job search success.
Are you encountering subtle signs of age bias in your job search? Let's work on your strategy together! Lisa
Start with a free formatted resume (that removes age references! :)