Algorithmic Hiring: This Technology Could Make Or Break Your Recruitment

Technology has always impacted recruitment. Now, new technology threatens to shake up the industry almost as much as when Monster invented the online job board 30 years ago – algorithmic hiring. At its most basic, algorithmic hiring involves a computer selecting candidates. The approach is gaining popularity in the US, especially among organisations aiming to streamline hiring processes and reduce human biases. But how exactly does algorithmic hiring work (does it work?), and even if it does, is it suitable for your company? In this article, we’ll break down the key aspects of algorithmic hiring—its benefits and challenges—and provide practical advice to help you decide whether it’s a worthwhile investment for your recruitment strategy.

What Is Algorithmic Hiring?

Algorithmic hiring uses computers – AI and machine learning, to evaluate a candidate’s suitability based on various data points. They might match how a candidate’s skills and background compare to your existing top performers in similar roles, predicting their likelihood of success and retention within your company and essentially looking to clone your best people. It’s a data-driven method of forecasting future outcomes based on past performance.

Computer Says Yes: The Benefits of Algorithmic Hiring

Increased Objectivity: Subconscious biases can influence traditional hiring processes, such as favouring candidates with similar backgrounds or gender. Algorithms focus purely on data, helping to reduce these biases. In theory they should be the ultimate blind hiring. Human judgments are often biased, but algorithms can help minimise those tendencies.
Higher Retention and Performance: By comparing things like problem-solving ability and temperament, businesses can hire candidates more likely to succeed and stay long-term. Even more granular data, such as keystroke patterns, can give insights into a candidate’s potential.
Time and Cost Savings: Ideally, these Data-driven insights allow recruiters to narrow down candidates more efficiently, reducing the time and costs associated with long hiring processes. If practical, fewer poor hires also mean lower turnover costs.
Improved Employee Quality: Some companies report that hiring algorithms lower turnover rates and improve overall quality scores, so there is some evidence that algorithms can enhance both retention and workforce performance.

Computer Says No: The Challenges of Algorithmic Hiring

While the potential benefits are tempting, algorithmic hiring also has its challenges. Here are some key issues to consider:

Bias Isn’t Eliminated – and may become hidden: Algorithms promise objectivity but are only as good as the data they’re trained on. If the data reflects existing biases—such as overrepresenting a particular demographic—the algorithm might unintentionally perpetuate and magnify those biases.
Overreliance on Data: Algorithms shouldn’t make the final hiring decision. While some companies report hiring managers who overrule the computer may have higher attrition, keeping the human element is essential. You must retain that agency and responsibility. It is critical to balance algorithmic insights with human judgment.
Manager Resistance: Convincing managers to adopt algorithmic hiring is a challenge. Experienced hiring managers want to trust their instincts. That’s not always a bad thing. Involving managers in the algorithm’s development, asking them to identify top performers and contribute to the criteria. This approach helped reduce resistance by making managers feel included in the process.
Limits of Data: Algorithms work best when large amounts of data are available. For senior roles, where fewer data points exist, their effectiveness decreases. Executive hiring often requires a level of judgment that algorithms currently cannot replicate.

How to Implement Algorithmic Hiring Successfully

If you’re ready to try algorithmic hiring, here’s how to ensure a smooth integration:

Start with Manager Involvement: Include hiring managers early to encourage buy-in. Get them to shape the algorithm’s criteria; they’ll see it as a complement to their instincts rather than a replacement.
Monitor for Bias: Review and validate your algorithm regularly to avoid introducing unintended biases. Be prepared to adjust data sets and retrain the model when needed.
Balance Data with Human Judgment: Algorithms shouldn’t ever be the only decision-maker. They can provide insight, but human judgment is essential. Ensure that any exceptions made to algorithmic recommendations are tracked, documented and justified.
Provide Training: HR professionals must understand how algorithmic tools work, the risks involved, and how to incorporate them into a fair and legally sound hiring process.

Conclusion: Is Algorithmic Hiring Right for You?

Algorithmic hiring is here. It will transform recruitment. But it’s crucial to approach it cautiously, with human oversight, regular checks for bias, and legal advice on liability. The laws are still catching up in the UK, and there are many valid concerns over discrimination. Companies using tech to hire still need to ensure their systems don’t result in discriminatory practices based on protected characteristics. The UK Information Commissioner’s Office (ICO) has emphasised that employers must be transparent about hiring decisions and ensure algorithms are fair, accountable, and explainable. There are growing calls for more explicit regulations addressing algorithmic bias in employment. If you’re considering adopting algorithmic hiring tools, test them in lower-risk areas of your recruitment process and get expert advice to ensure you’re using this technology to its fullest potential, but safely.