The Pitfalls of AI-Driven Hiring: Are the Best Candidates Being Left Behind?

In the ever-evolving recruitment landscape, companies increasingly turn to artificial intelligence (AI) to streamline their hiring processes. However, as firms rely more on AI-driven hiring platforms, a disturbing trend is emerging: highly qualified candidates are overlooked and excluded from job opportunities. Despite the initial promise of reducing biases in recruitment, these AI tools are raising concerns about their accuracy and potential to screen out top-notch candidates.

The Rise of AI in Hiring:
Body-language analysis, vocal assessments, gamified tests, and CV scanners are just a few examples of companies' tools to screen job applicants using AI recruiting software. A late 2023 IBM survey revealed that 42% of companies globally were already using AI screening to enhance their recruiting and human resources, with an additional 40% considering integrating the technology.
Unintended Consequences:
While adopting AI in recruitment was anticipated to eliminate biases in the hiring process, some cases suggest the opposite is occurring. Hilke Schellmann, an expert in AI and an assistant professor of journalism at New York University, points out that these tools might inaccurately screen highly qualified candidates, preventing them from securing roles. The potential harm is significant, with some job candidates already sharing their negative experiences with AI-driven hiring platforms.
Real-Life Examples:
Anthea Mairoudhiou's case in 2020 serves as a poignant example of the unintended consequences of AI in hiring. After being furloughed during the pandemic, she re-applied for her position and underwent evaluation using an AI screening programme called HireVue. Despite scoring well in skills evaluation, the AI tool negatively assessed her body language, leading to her permanent job loss. Similar complaints against AI platforms have been filed by other workers, highlighting the potential harm and bias in the recruitment process.
Systemic Flaws:
One major concern is that these AI algorithms are often trained on a specific type of employee, potentially eliminating candidates with diverse backgrounds or credentials. Schellmann's research has unveiled systemic flaws, including cases where candidates were unfairly rejected based on irrelevant criteria such as hobbies or biassed evaluations.
Opaque Selection Criteria:
The lack of transparency in the evaluation process further exacerbates concerns. Job candidates need to be more informed about why AI tools reject them, making it challenging to address potential biases or flaws. Schellmann's experiments demonstrated instances where seemingly irrelevant factors played a role in the assessment, such as a candidate receiving a higher rating for irrelevant language skills while being downgraded for relevant credentials.
Concerns for the Future:
As the use of AI in hiring continues to spread, there are growing fears that the negative impact on job applicants will escalate. Schellmann emphasises that while biassed human hiring managers can harm many people, an algorithm used across all applications at a large company could harm hundreds of thousands of applicants.
Call for Industry-Wide Measures:
Schellmann advocates for industry-wide "guardrails and regulation" to address the current problems associated with AI-driven hiring. Without intervention, she warns that AI could exacerbate workplace inequalities in the future. Sandra Wachter, a professor of technology and regulation at the University of Oxford, supports this call for action and is working on tools like the Conditional Demographic Disparity Test to help companies identify and rectify biases in their algorithms.

As companies embrace AI-driven hiring platforms to make their processes more efficient and unbiased, it is crucial to acknowledge and rectify the unintended consequences. The concerns raised by experts like Schellmann and Wachter highlight the need for industry-wide regulation and ethical considerations to ensure that AI in hiring leads to fair, equitable, and merit-based decisions, ultimately benefiting both companies and job candidates alike.