Maximize the Benefits of AI for Hiring While Avoiding the Pitfalls

Apr 25, 2024

More and more companies are turning to artificial intelligence tools to handle complex business tasks—from creating targeted marketing to producing blog content.

One of the more prevalent use cases for AI is in the HR space, specifically for hiring. This technology can enhance candidate screening by instantly reviewing resumes and other materials to find individuals with the best skills and experiences for specific jobs.

But, as a recent article points out, there are also risks this technology can pose when it’s used for talent management. In February 2024, for example, a man in California filed a class-action lawsuit against Workday, claiming the tools the company uses are discriminating against applicants who are African American, older than 40, and/or disabled.[1] Experts predict more such lawsuits will be coming, especially as AI gets more sophisticated and will be applied more frequently to other HR practices such as layoffs and succession planning.

The thing is, it’s not AI technology that poses the risk but the data that AI software is picking up. Most of these tools use “word scraping” – scanning the language included in job ads, job board algorithms, resumes, and applicant-tracking software to prioritize candidates. This is where the bias starts filtering in. If someone uses more sophisticated words in their application, they may have an advantage. If someone’s resume overstates their experience or accomplishments, they may have an advantage. 

If a company requires a college education, this may also skew the AI filters, eliminating otherwise qualified people. Keep in mind that Bill Gates and Steve Jobs never graduated from college! And hiring managers are aware of the problem. A Harvard survey found that 9 out of 10 executives believe software filters prevent them from seeing good candidates. And an estimated 10 million workers are excluded from hiring discussions as a result.

This is why WAHVE doesn’t rely on resumes to help companies screen candidates for open positions. Instead, candidates complete detailed applications that capture both hard and soft skills and work experience. These are compared to the detailed skills and experience requirements submitted by the hiring companies. Our proprietary machine learning model then goes to work, pulling out top candidates for the position.

But that’s still not the full solution – because candidates often exaggerate their skills and experience. In fact, a recent ResumeLab survey of over 1,900 job applicants found that 70% confessed to lying on their resumes.[2] So, we validate a candidate’s skills using a series of assessments. And the applicants who pass this filter are then blind-interviewed and reference-checked by industry experts. The result is a bias-free shortlist of highly qualified candidates.

To sum up, AI is a powerful tool that can save time and predict successful outcomes in the hiring process. But it’s not perfect. To avoid bias and false data, couple AI with a thoughtful, people-based process for validating a candidate’s experience, credentials, and fit.

If your business uses AI for HR practices, how do you ensure it remains free from bias and is fair and effective? 




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