Talent Hunter’s Liam Whelan discusses the positives and the common pitfalls of the growing AI recruitment space.
Far removed from the technology space, Liam Whelan, the founder and CEO of AI-powered recruitment platform Talent Hunter, initially got his start in the hospitality and recruitment sector.
“While that background might seem distant from AI recruitment, it was actually the ultimate catalyst,” explained Whelan. “My time in hospitality was defined by constant, high-stakes staffing, process optimisation and resource allocation. I saw first-hand the crippling impact of a poor hire on team morale and service quality and the sheer inefficiency of trying to fill critical roles manually.”
Under constant pressure to scale and grow rapidly, but maintain quality, Whelan found traditional hiring tools were becoming slow, showing bias and were increasingly inconsistent.
“This operational frustration, combined with my extensive training and certification as a Lean Six Sigma Green Belt, led to the key realisation, recruitment, fundamentally a process of predicting human success, was decades behind in technology. My passion shifted to finding a way to remove the waste and variability from this process.”
And so he began to upskill in machine learning, as a means of targeting outdated manual processes via data-driven foresight and what he believed to be algorithmic fairness. Thus Talent Hunter was born, a recently launched Irish AI-powered recruitment company designed to address the pain points Whelan “experienced daily”.
Irish excellence
Ireland has firmly cemented itself as an internationally recognised global hub for technological innovation, but for Whelan, how it navigates the next phase is incredibly important.
“It is absolutely crucial,” he said. “Ireland is already a global technology hub, but to transition from being a home for multinational tech operations to being a global leader in AI innovation, we must prioritise two things.”
The first being ethical AI leadership, where the country can showcase its commitment to global AI policies and development. By focusing on good practices and behaviours, Ireland differentiates itself and attracts future-focused companies.
According to Whelan, the second priority should be talent development, whereby Ireland’s leaders in this space “aggressively invest in upskilling [the] workforce and university programmes in data science and machine learning to ensure a sustainable, locally-grown talent pipeline that fuels the next wave of indigenous AI start-ups”.
“A strong, principled position in the AI revolution will solidify Ireland’s economic future and brand on the world stage,” said Whelan.
Is it an automated world?
Artificial intelligence can reasonably be described as a bit of a mixed bag. Some of the innovations we are seeing, particularly in areas such as the healthcare sector, have the potential to change lives for the better.
In response to the growing popularity of AI, many organisations and institutions are investing heavily in AI-driven tech, but it stands to reason that not every sector is in need of a drastic transformation. But for Whelan, recruitment has certainly emerged as a field in which change is warranted.
He explained that AI in recruitment addresses three major challenges that often occur in the modern workplace, for example “the volume problem”. Whelan explained that this is where the ease of applying for a role has resulted in what could be considered an overwhelming volume of applicants, making manual reviews impossible and leading to burnout for human recruiters.
He also said that AI can address a bias problem, where, be it consciously or unconsciously, human recruiters make snap decisions based on irrelevant information such as the gender of the applicant, the college they went to, or the region that their name originates from.
Lastly there is the skills gap issue that can be addressed, where many new roles require niche or hybrid skills that traditional resumes can fail to capture. “AI can perform real-time skill-mapping and analyse non-traditional experience, like project work or online courses, to find the right person,” said Whelan.
Working out the problems
That is not to suggest that AI itself doesn’t create its own challenges. Whelan noted the potential for algorithmic bias where the historical data used to train the model reflects outdated ideals, for example that certain roles within STEM are better suited to male candidates.
Additionally a lack of transparency could cause both companies and candidates to distrust a decision they don’t fully understand, leading to poor adoption and even legal risks.
To overcome these issues, Whelan explained that users and organisations have to commit to implementing bias audits and de-biasing efforts, whereby they implement “periodic algorithmic audits to ensure the models are prioritising skills and competencies over protected characteristics”.
And never forget that an AI model is only ever there to assist, not to take over. Whelan said the final decision should always be made after the work completed by the AI model is reviewed, “ensuring empathy, nuance and strategic oversight”.
“The future of work is not about replacing the human element, but about amplifying it.”
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