6 minute read
Posted by Emily Hill on 22 May 2026
Most discussions about AI in hiring tend to fall into two extremes. Either AI is positioned as a solution that removes bias and improves hiring quality, or it is framed as a threat replacing recruiters entirely.
In reality, most automated decision making (ADM) tools sit somewhere in the middle. They rarely replace hiring decisions completely, but they increasingly influence which candidates progress, which candidates are prioritised, and ultimately who gets hired.
The UK Information Commissioner’s Office (ICO) defines automated decision making as:
“a decision made by technological means without human involvement.”
Under GDPR Article 22, individuals have the right not to be subject to decisions based solely on automated processing where those decisions produce legal or similarly significant effects.
In recruitment, this may include candidates being:
In practice, though, hiring automation is rarely fully automated. Most organisations still involve recruiters somewhere in the process. The more important question is whether that human involvement is genuinely meaningful, or whether recruiters are simply validating recommendations produced by the system.
An automated decision making tool is any system that influences hiring decisions using rules, scoring models, algorithms, or AI.
This can include systems that:
Importantly, not all ADM tools are AI driven. Even relatively simple rules based systems can materially affect hiring outcomes if they determine who progresses and who does not.
The growth of ADM tools is being driven by a combination of operational pressure and commercial demand.
Recruiters are dealing with significantly higher application volumes, alongside increasing numbers of AI generated CVs and low intent applications. At the same time, organisations are under pressure to reduce time to hire and improve recruitment efficiency.
As a result, automation is increasingly being positioned as the only scalable way to manage modern hiring pipelines.
The challenge is that many hiring systems optimise primarily for speed and throughput rather than long term hiring quality. Systems are often designed to process applications faster and reduce recruiter workload, but are less commonly optimised around retention, performance, or whether candidates actually understand the reality of the role.
Most automated hiring systems fall into several broad categories.
These systems use fixed criteria such as years of experience, qualifications, location, or mandatory screening questions. They are transparent and predictable, but can also be blunt and exclusionary.
Many platforms assign weighted scores to candidate attributes such as skills, experience, assessments, or interview responses. Candidates are then ranked based on total score.
This is one of the most common forms of hiring automation.
Some systems evaluate candidates using work samples, scenarios, or simulated tasks. While often more predictive than CV screening alone, many so called “realistic” assessments still simplify the actual reality of the role.
More advanced systems use historical hiring and performance data to predict candidate success. These models can identify patterns humans might miss, but they can also reinforce historical bias or flawed hiring assumptions at scale.
At the far end of the spectrum are systems capable of automatically progressing or rejecting candidates without meaningful human review. This is where the highest regulatory and reputational risks tend to emerge.
The regulatory conversation around AI in recruitment is also evolving quickly.
Chris Platts recently participated in an ICO roundtable focused on AI and automated decision making in recruitment, attending both as CEO of ThriveMap and Chair of the ARTP Standards Committee.
The discussion focused on:
One of the major challenges discussed across the industry is that current regulation was largely written before modern AI assisted hiring workflows became common. Most recruitment processes are no longer fully manual or fully automated. Instead, they combine recruiter judgement with scoring systems, AI assisted recommendations, assessments, and workflow automation.
This creates difficult questions around what genuinely counts as “meaningful human involvement” under Article 22, particularly where recruiters routinely rely on automated rankings or recommendations when making decisions.
As hiring technology continues to evolve, there is increasing pressure on employers and technology providers to ensure hiring processes remain transparent, explainable, and open to challenge rather than simply efficient.
Most buying processes focus too heavily on product features and not enough on decision quality and governance.
Organisations should be able to clearly explain:
If a candidate asks why they were rejected, the organisation should be capable of providing a meaningful explanation rather than simply pointing to a score generated by the system.
Companies should also be realistic about what success actually means. Many systems optimise for proxy metrics such as assessment scores, CV keywords, or interview conversion rates. Far fewer optimise for long term retention, job performance, or candidate fit.
The biggest risk is not simply bias.
Often, the larger issue is false confidence.
Automated systems can create the impression that hiring decisions are objective, scientific, and data driven, even when they are still heavily shaped by historical assumptions, subjective definitions of success, or flawed training data.
Technology does not automatically improve hiring quality. In many cases, it simply scales existing hiring logic more consistently and at greater speed.
At ThriveMap, we believe hiring technology should help humans make better decisions, not remove human decision making altogether.
That means designing hiring processes around clarity, structure, and realistic understanding of the role rather than relying purely on automated scoring or filtering.
Our approach focuses on:
As AI and automation become more common in recruitment, we believe the organisations that perform best long term will be those using technology to support human judgement rather than replace it.
Because ultimately, hiring is still a human decision.
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