Using AI in Recruitment

Most people think having a human approve an AI decision means the decision is safe. It does not. 👀 There is a term for what actually happens when humans rubber stamp AI outputs under time pressure. Automation bias. It is one of the most documented and underreported risks in enterprise AI right now. After 13 years and 200+ deployments, here is what I have learned about building genuine oversight into AI systems. The human reviewing an output needs three things to actually be in the loop. They need to understand what they are reviewing. They need the context to catch what the model gets wrong. And they need to be genuinely empowered to say no without institutional pressure to simply keep moving. Most organisations have none of those three in place. They have a signature process. That is not the same thing. Before any high-stakes AI output reaches a decision point in your organisation, ask these questions. ➡️ Does the person approving this understand the underlying data well enough to catch an error? ➡️ Is there time built in for genuine review or just enough time to click approve? ➡️ What happens if someone says no? Is that genuinely supported? If the answer to any of those is no… you do not have human oversight. You have automation bias with a human signature attached. What does genuine human oversight look like in your organisation right now? #ai #leadership #futureofwork #artificialintelligence #aistrategy #teamhuman #intellectualatrophy #criticalthinking

Your AI recruiting agent or use case might be brilliant. It might also be illegal. If your AI screens, ranks, or evaluates candidates - you're operating in an increasingly actively regulated environment. And not just in the US. NYC requires annual bias audits. Illinois requires notice. California requires 4-year data retention. Colorado requires impact assessments with $20,000 per violation penalties. The EU classifies all recruiting AI as high-risk. South Korea's AI Basic Act explicitly lists hiring as high-impact. Brazil and Chile have GDPR-style rights against automated employment decisions. Singapore's Workplace Fairness Act covers AI-driven hiring decisions. This isn't a US-and-EU issue. It's global. Something else you need to look out for - your compliance is only as strong as the gap between your published AI notice and what your people actually do. A recruiter pastes a resume into ChatGPT on a busy Tuesday. Or simply uses their company-approved solution in a way that wasn't approved. That tool/use case hasn't been audited. There's no notice. No audit trail. The employer is still liable. I wrote a full breakdown of the regulatory landscape - US, EU, and the global wave most people don't see coming - and what TA teams need to do about it. Check it out 👇

People are quickly discovering AI as a powerful way to augment and accelerate their job search efforts by overcoming the often mechanical and robotic application process. Brilliant! Good for them. Recruiting functions are being overwhelmed by applications and the threat of potential bot applicants. Predictable but legitimate and understandable challenge. To combat this trend, a growing number of companies are looking for AI to filter out candidates suspected of using AI in their applications. Wait, WHAT!?! If you’re considering this at your company, please take a strategic pause. Few things to consider. First, if someone or something is making it all the way to offer without a legitimate human interaction, you may want to scrap your entire TA approach and start over. Second, trying to detect AI specifically trained to mimic human behavior and language will leave you eliminating more qualified human candidates than it is catching AI criminals. Finally, consider why you would legitimately want to eliminate a candidate already demonstrating one of the most critical job skills for the modern age: AI problem-solving. Here is some additional food for thought. If you’re legitimately getting overwhelmed by what you believe are AI applications, it’s an indication your application process is far too robotic today, which is why it’s such an easy target. Accept that the robotic parts are what they are: a check-the-box activity. Make them quick and easy for a human and move on. After that, explore how you can augment your robotic process by using AI to assess skills versus someone’s head knowledge or ability to fill out a form. AI is making realistic job simulations scalable. Wouldn’t you rather have a better indication of the person’s ability to perform the job anyway? From there, ensure you maintain plenty of human oversight in your process. I agree AI can automate and speed things up, but in the AI age, interpersonal and human skills are what you need from humans, so make sure you have humans involved in hiring other humans for human work. The AI future has so much potential for good, but not when we use it as a quick fix to plug cracks in a broken dam. Instead, we need to see how it can reimagine solutions to the root problems. #BigIdeas2024

The recruiter’s job is changing - quietly, but fundamentally. Not because AI is taking over sourcing or screening (we’ve been automating that for a decade). But because recruiters are about to inherit something new: governance. When an AI system ranks, recommends, or rejects a candidate, someone in the organisation now has to be accountable for how that decision was made. That someone will increasingly be the recruiter. Not Legal. Not IT. The recruiter — acting as the human reviewer of AI-driven decisions. And it’s not optional. The EU AI Act, New York’s bias-audit laws, and now the UK Data Use and Access Act all converge on one requirement: ➡️Every “high-risk” AI system must have meaningful human oversight by a qualified reviewer - someone who understands the context, purpose, and potential impact of the system’s decisions. In hiring, that’s not your privacy lawyer or data engineer. It’s the recruiter. They’re the only ones close enough to the process to spot when the AI gets it wrong - when “efficiency” quietly turns into exclusion. The recruiter’s future isn’t about doing more with AI. It’s about knowing when and how to challenge it. That’s what responsible automation really looks like. I'm helping TA teams make that shift - from users of AI to qualified overseers under these emerging regulations. Recruiters need to build this governance capability now, before they find themselves unable to adapt to the future of their role. 💡 Question for you: If your AI tools are already screening or scoring candidates, who’s the qualified reviewer in your organisation?

Amazon’s hiring AI once rejected qualified women and preferred men. Here’s why: Paola Cecchi-Dimeglio, a Harvard lawyer and Fortune 500 advisor, has a warning for HR: If you ignore AI bias, you scale discrimination because it learns our prejudice and amplifies it in hiring and performance decisions. Remember Amazon's hiring algorithm? It systematically favored male candidates because it learned from historical hiring data that was already biased. The tool was discontinued, but the lesson remains relevant for every organization using AI today. Dimeglio identifies three critical sources of bias: 1. Training data bias: When AI learns from unrepresentative data, it produces skewed outcomes. For example, generative AI models underrepresent women in high-performing roles and overrepresent darker-skinned individuals in low-wage positions. 2. Algorithmic bias: Flawed data leads to biased algorithms. Recruitment tools may favor keywords more common on male resumes, perpetuating gender disparities in hiring. 3. Cognitive bias: Developers' unconscious biases influence how data is selected and weighted, embedding prejudice into the system itself. Paola's solution framework for HR leaders: ✅ Ensure diverse training data – Invest in representative datasets and synthetic data techniques  ✅ Demand transparency – Require clear documentation and regular audits of AI systems  ✅ Implement governance – Establish policies for responsible AI development  ✅ Maintain human oversight – Integrate human review in AI decision-making  ✅ Prioritize fairness – Use methods like counterfactual fairness to ensure equitable outcomes  ✅ Stay compliant – Follow regulations like the EU's AI Act and NIST guidelines As Paola emphasizes: "HR leaders, as the gatekeepers of talent and culture, must take the lead on avoiding and mitigating AI biases at work." This isn't just about fairness, it's about achieving better outcomes, building trust, and protecting your organization from legal and reputational risks. The question isn't whether AI has bias. It's whether you're doing something about it. How is your organization addressing AI bias in HR processes? Let's discuss.

I grew up watching machines go rogue🤖 Now I help companies stop that from happening in real life. 🦾 Growing up, I loved watching sci-fi movies. In the 90s, the theme was always the same: man creates a scientific marvel, man loses control over said marvel… cue the running, screaming, and inevitable bloodshed. As a kid, I lapped up those stories, which always hammered home one moral: humans messing with the laws of nature never ends well. Fast forward to today, and I find myself advising companies on a very real version of that narrative, which is using AI in HR. With AI tools increasingly used to monitor performance and even flag employees for dismissal, the question isn’t just “can we do this?” but “should we? And how do we do it fairly?”. I recently shared my views on this topic with HRD Asia (link to article in the comments below). In general, HR teams must get the following right: 🔹 Transparency: Employees should know how their performance is being assessed and what data is being used. 🔹 Human Oversight: AI should assist human judgment. It can never replace it. Accordingly, a meaningful review process is essential. 🔹 Vendor Accountability: Employers must understand how third-party tools work and ensure they don’t produce biased outcomes. 🔹 Appeal Mechanisms: Employees need a way to challenge decisions influenced by AI. 👨⚖️ In my practice, I’ve already seen clients ask whether an AI-generated score is enough to justify dismissal. My answer? Not without human validation and a clear explanation of how the score was derived. Implementing a Human-In-The-Loop approach to any automated scoring tools would also ensure that any employment decision is validated by an employee who can justify the AI-generated recommendation. This is especially important in employment decisions relating to summary dismissal which carry significant legal risks, such as wrongful dismissal claims. While there is no hard and fast rule when it comes to determining the appropriate level of intervention, the key principle is that the reviewer must be able to understand how the AI arrived at its decision and the individual must have the authority to override it if necessary. The review process should not be a mere formality or rubber-stamping exercise; it must serve as a meaningful check to ensure fairness and accountability. As the use of AI tools in HR is increasingly becoming popular, the time to get familiar with the legal issues surrounding its use is now. Build internal safeguards, update your policies, and make sure your HR team understands the tools they’re using. Because if those 90s sci-fi movies have taught us anything, it’s that leaving machines to make human decisions rarely ends well. Would love to hear how you are balancing AI efficiency with fairness, do share your thoughts below! #AIinHR #WorkplaceFairness #SingaporeHR #HRCompliance #AIethics #HumanOversight #EmploymentLaw #SciFiMeetsReality

AI can cut hiring time by 80% (McKinsey & Company), but at what cost? Automation is faster, smarter, more efficient, but if we’re not careful, it’s also more biased, less human, and dangerously flawed. As a result, HR leaders now hold a double-edged sword. + Use AI wisely, and it transforms recruitment.  + Use it blindly, and it reinforces the very problems we’re trying to solve. According to McKinsey, AI-driven tools have increased recruiting efficiency by 80%, yet 76% of job seekers say the hiring experience impacts whether they accept an offer. Speed matters.  But so does fairness.  So does trust. Because efficiency means nothing if candidates feel reduced to a data point. AI is only as fair as the data it learns from. And if that data carries bias? AI will replicate it, at scale. I still remember an instance from two years back: a candidate with an unconventional career path, a late-degree switch, a few gaps, non-traditional experience was filtered out by an AI-automated software. On paper, they weren’t a fit. In reality, they were exactly what the company needed. But imagine how many great hires are being lost because no one is watching? AI can analyse resumes, predict job fit, and streamline hiring like never before. But it cannot replace the human judgment, emotional intelligence, and ethical responsibility that recruiters bring to the table. So, how do we use AI without losing the human element? ✅ Train AI to spot bias, not amplify it: AI learns from past data. If that data carries bias, AI will replicate it. Audit algorithms. Diversify data sets. Ensure AI isn’t just fast, but fair. ✅ Use AI to enhance decision-making, not replace it: Predictive analytics can tell you who to interview. But only humans can assess cultural fit, build trust, and make final hiring decisions. ✅ Create transparency in hiring: Candidates should know when AI is evaluating them. If an algorithm rejects someone, recruiters should intervene, not blindly trust the machine. ✅ Prioritise candidate experience: Chatbots and automation can provide instant updates, but real conversations build relationships. The best hires don’t just want a job, they want to feel valued. AI isn’t the future of recruitment. Humans + AI is. The goal isn’t to replace recruiters, it’s to empower them to be better, faster, and fairer. Because at the end of the day, great hiring isn’t just about efficiency. It’s about people. #aiinhr #ethicalhiring #hrleadership Puneet Chandok, Navnit Singh, Rishi Khandelwal, Shailja Dutt

The “Human in the Loop” Illusion Enterprises often treat “human in the loop” as a safety net or the magical guarantee that AI won’t make harmful mistakes. But in practice, HITL is one of the most misunderstood and poorly executed components of enterprise AI governance. On paper, HITL means oversight. In reality, it frequently means rubber-stamping. Humans trust computer output more than they should. Psychologists call it automation bias: if something comes out of a system, people assume it’s probably correct. Combine that with another very human trait : no one enjoys cleaning up someone else's mess and HITL quickly devolves into “approve unless it looks obviously broken.” Add fatigue on top of that and oversight collapses even further. As AI systems scale, they generate more items for humans to review, and once confidence increases even slightly, humans spend less time checking… until something breaks. I saw this play out in a finance team using an AI invoice classifier. During the first month, reviewers carefully checked every field. Accuracy looked good and everyone was impressed. By the third month, attention had slipped, of course, not intentionally, just naturally. The model began confusing vendor names with similar abbreviations, and no one caught it. When reconciliation eventually blew up, the team realized the truth: the humans weren't “in the loop”; they were downstream casualties of a loop no one was actively monitoring. This is the core problem: HITL can dilute accountability instead of strengthening it. Everyone assumes one or the other party (the model or the reviewer) will catch the error. And in that gap of shared responsibility, errors slip through. The solution is not more humans or more prompts. It is proper governance, which starts with treating HITL as a designed process, not a checkbox. Roles, responsibilities, edge-case handling, escalation paths, sample-based audits, and fatigue-aware workloads all need to be deliberately engineered. And above all, HITL must be paired with AI evaluations. You cannot rely on ad-hoc human judgment to detect drift, edge-case hallucinations, or degradation under real workload conditions. Structured evals tell you what the model can do, what it cannot do, and when humans genuinely add value. HITL gives only the illusion of safety. Unfortunately, illusions have a way of breaking at exactly the wrong time. #EnterpriseAI #PracticalAI #HITL #SiliconValley Cognida.ai

𝗢𝗻𝗲 𝗧𝗵𝗶𝗻𝗴 𝗧𝘂𝗲𝘀𝗱𝗮𝘆: 𝗘𝗽𝗶𝘀𝗼𝗱𝗲 𝟭𝟯 𝗧𝗵𝗶𝗻𝗸 𝘆𝗼𝘂𝗿 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗺𝗲𝗻𝘁 𝘁𝗲𝗰𝗵 𝗶𝘀 𝗻𝗲𝘂𝘁𝗿𝗮𝗹? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗴𝗮𝗶𝗻. Before your next hiring round - especially if you’re using AI or automation - there’s one thing to check: 𝗛𝗼𝘄 𝗮𝗿𝗲 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀 𝗯𝗲𝗶𝗻𝗴 𝗿𝗮𝗻𝗸𝗲𝗱 𝗼𝗿 𝗳𝗶𝗹𝘁𝗲𝗿𝗲𝗱? Many tools score CVs based on career gaps, education history, or “typical” profiles from past hires. Sounds efficient. But it’s not neutral. It often means penalising women, carers, and anyone with a non-linear path - which can amount to 𝗶𝗻𝗱𝗶𝗿𝗲𝗰𝘁 𝗱𝗶𝘀𝗰𝗿𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 under the UK Equality Act. Under the new EU AI Act, tools like this are classed as 𝗵𝗶𝗴𝗵 𝗿𝗶𝘀𝗸. That means mandatory transparency and human oversight. 𝗪𝗵𝗮𝘁'𝘀 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼? Ask your provider how the system scores candidates. Request a fairness audit. If they can’t answer clearly - that’s your answer. Let’s not automate the very barriers we’re trying to dismantle. #InclusiveHiring #GenderEquity

AI systems are often described as “objective.” But AI learns from human data, human decisions, and human systems and so the assumption in essence is unfounded... #Procurements role is to ensure that objectivity in the pursuit of competitive advantage and #value can use human judgement to counter biases such as; 🔹 Data bias When datasets contain gaps, imbalance, duplicates, errors or historical skew. 🔹 Algorithmic bias Bias introduced through model design choices, optimisation goals, or feature selection. [Even with balanced data, models can still produce unequal outcomes]. 🔹 Interaction bias Bias that emerges through user interactions and feedback loops and sometimes reinforce harmful patterns in the process. 🔹 Societal / representation bias When broader cultural or institutional inequities are reflected in data and outputs, this can be replicated and challenge objectivity. The important insight highlights that there is usually systemic, layered, and reinforced outcomes across the AI lifecycle. Example consequences are real: 📌 Supplier selection filtering can be biased and remove potential unfairly 📌 Historical data adversely affecting issues that have been addressed and improved relative to key criteria or data on suppliers/goods/routing, etc. 📌 Financial algorithms amplifying inequality in results or benchmarks 📌 Recommendation systems reinforcing stereotypes So how can organisations reduce AI bias? ✅ Use more diverse and representative datasets - not just one! ✅ Audit models regularly across diverse geographies ✅ Combine technical testing with human oversight ✅ Be clear where augmentation and automation are in place ✅ Monitor feedback loops after deployment ✅ Build fairness and ethics into AI governance from the start Post based on an article by Center for Behavioral Decisions (CBD) Feel free to share and comment: CIPS - The Chartered Institute of Procurement & Supply Carly Read Dr Howard Price PhD FRSA MSc DMS MCIPS Chartered Kieran Delaney Tupuna Tapanainen Alexa Bradley Ben Farrell MBE James Moore Dan Aston AbdulAziz AlOlayan MCIPS Mike Cargiulo, MBA Muneera Al Hammadi 🇦🇪 Divyabh Mishra