Using AI in Recruitment

Think of AI as your next teammate: fast, tireless, and incredibly efficient. Not a competitor or a threat. Just a really efficient team member who understands what you want to be done and executes it in minutes, freeing up your time to do more. Does that mean it will replace you? I still remember the first time AI tools were introduced to our talent teams. There was curiosity, of course, but also hesitation. Would technology replace the recruiter’s role? Would it make hiring transactional? As someone who has spent years building talent functions, I see AI a bit differently. When you are working in a team, collaboration does not become competition. AI-driven tools create space: to think deeper, evolve faster, and build better. Today, at GlobalLogic, AI is quietly woven into how we hire and train, not as a replacement, but as a force multiplier. Because technology does not build great teams. People do.  > AI can spot patterns, but it is still human leaders who spot potential.  > AI can suggest matches, but it is human insight that builds trust.  > AI can speed up steps, but it is human mentorship that builds careers. AI has its limits, but it removes the limits of what people can imagine, connect, and create. #AI #teams #technology #growth #talentacquisition

One of the benefits of being the Head of Responsible AI at Warden AI is working with hiring platforms that take governance seriously as. For these companies, it's an operational discipline, not a marketing strapline. Yesterday, one of those clients, Jack & Jill, published a report on bias in AI hiring that’s worth paying attention to. What I found particularly strong is how clearly it mirrors what my own research from last year surfaced from the opposite direction. My work showed what happens when off-the-shelf LLMs are used for CV screening without constraints: inconsistent outcomes, shallow explanations, and serious governance risk. Jack & Jill’s report shows the counterfactual: → what changes when bias is treated as a design and monitoring problem. Their testing found: • Humans under time pressure showed demographic skew ~35% of the time • Off-the-shelf LLMs reduced this, but still shifted ~23% of decisions • A deliberately designed system, stripped of demographic signals, scored against a fixed brief, and continuously audited produced materially fairer outcomes The takeaway isn’t “AI good / humans bad”. It’s that fairness is not a property of a model. It’s a property of the system around the model - the constraints, the evidence, the monitoring, and the willingness to keep testing assumptions once the tool is live. That’s exactly where responsible AI in hiring is heading. Responsible AI in recruitment isn’t about claiming perfection. It’s about building feedback loops, audit trails, and the humility to test your own assumptions. Continuously. (sorry, Hung, kind of spoiling my thoughts for the webinar later) Matthew Wilson shared the full report in his post yesterday, it’s well worth reading in full if you’re evaluating or deploying AI in recruitment (link to the report in the comments 👇)

𝗔𝗜 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗵𝗼𝘄 𝘄𝗲 𝗵𝗶𝗿𝗲, 𝗯𝘂𝘁 𝗶𝘀 𝗶𝘁 𝗵𝗲𝗹𝗽𝗶𝗻𝗴 𝘂𝘀 𝗯𝘂𝗶𝗹𝗱 𝗶𝗻𝗰𝗹𝘂𝘀𝗶𝘃𝗲 𝘁𝗲𝗮𝗺𝘀? Many companies I partner with, including Fortune 500s, have started using AI for hiring from resume screening to video interviews. And I'm a big advocate for these tools because they help us hire faster and more fairly. But here's what many may not realise. It's not about just using AI. It's about using it the right way. This is really important because that ensures that candidates are all truly assessed for their skills. So if you are wanting to build an inclusive hiring process with AI, here are 5 ways to get started: 1️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘆𝗼𝘂𝗿 𝗴𝗼𝗮𝗹𝘀. Get clear about what fair and inclusive hiring means for your team before adding AI. This way you'll have clear measures of success too. What gets measured, gets tracked. 2️⃣ 𝗨𝘀𝗲 𝗱𝗶𝘃𝗲𝗿𝘀𝗲 𝗱𝗮𝘁𝗮. We all know that AI is only as good as what we feed it. To give yourself the best chances, make sure your data input reflects real diversity, across race, gender, age, ability, and more. 3️⃣ 𝗣𝗮𝗿𝘁𝗻𝗲𝗿 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝘁𝗲𝗰𝗵 𝘃𝗲𝗻𝗱𝗼𝗿𝘀 Ask how they test for bias and what proof they have their tools are fair. Inclusion is a shared responsibility. 4️⃣ 𝗖𝗵𝗲𝗰𝗸 𝘆𝗼𝘂𝗿 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗼𝗳𝘁𝗲𝗻. I always say, everything is data. Look for patterns in who gets filtered out. One client found their AI was missing career changers—something we only caught by reviewing the data. 5️⃣ 𝗞𝗲𝗲𝗽 𝗽𝗲𝗼𝗽𝗹𝗲 𝗶𝗻𝘃𝗼𝗹𝘃𝗲𝗱. Yes, AI helps, but we still need humans making the big calls. Train your team to spot and correct bias, whether it comes from tech or people. What are some inclusive hiring practices you've seen? I'd love to hear your stories! #inclusivehiring #airecruitment #lfbalumni #diversityandinclusion  

Many leaders aim to use AI to promote diversity and inclusion, but not all understand how to manage the challenges it brings. And that’s where things can go wrong. Here’s the truth: AI can either be a tool for driving diversity or a source of unintended bias. Without the right approach, you risk: Bias creeping into hiring algorithms Overlooking diverse talent Creating a less inclusive workplace culture But it doesn’t have to be this way. 🔑 Here’s how leaders can leverage AI to drive diversity and inclusion: 1️⃣ Ensure AI Systems are Bias-Free → Regularly audit AI systems to identify and eliminate biases that could affect recruitment, promotions, or workplace culture. 2️⃣ Use AI to Amplify Diverse Talent → AI can help uncover talent from underrepresented groups by focusing on skills and potential rather than traditional backgrounds. 3️⃣ Foster a Culture of Inclusivity with AI → Use AI-driven insights to create policies and initiatives that actively promote inclusion and belonging within your teams. 4️⃣ Invest in Continuous Learning → The landscape is always evolving. Regularly update AI tools and strategies to ensure they reflect the latest in diversity and inclusion best practices. AI can be a game changer for workplace diversity—but only with the right strategy and oversight. 👉 Ready to explore how AI can help you build a more inclusive workforce? Let’s connect and discuss ways to leverage AI responsibly for a more diverse future.

"five building blocks — conceptual and technical infrastructure — needed to operationalize responsible AI ... 1. People: Empower your experts Responsible AI goals are best served by multidisciplinary teams that contain varied domain, technical, and social expertise. Rather than seeking "unicorn" hires with all dimensions of expertise, organizations should build interdisciplinary teams, ensure inclusive hiring practices, and strategically decide where RAI work is housed — i.e., whether it is centralized, distributed, or a hybrid. Embedding RAI into the organizational fabric and ensuring practitioners are sufficiently supported and influential is critical to developing stable team structures and fostering strong engagement among internal and external stakeholders. 2. Priorities: Thoughtfully triage work For responsible AI practices to be implemented effectively, teams need to clearly define the scope of this work, which can be anchored in both regulatory obligations and ethical commitments. Teams will need to prioritize across factors like risk severity, stakeholder concerns, internal capacity, and long-term impact. As technological and business pressures evolve, ensuring strategic alignment with leadership, organizational culture, and team incentives is crucial to sustaining investment in responsible practices over time. 3. Processes: Establish structures for governance Organizations need structured governance mechanisms that move beyond ad-hoc efforts to tackle emerging issues posed in the development or adoption of AI. These include standardized risk management approaches, clear internal decision-making guidance, and checks and balances to align incentives across disparate business functions. 4. Platforms: Invest in responsibility infrastructure To scale responsible practices, organizations will be well-served by investing in foundational technical and procedural infrastructure, including centralized documentation management systems, AI evaluation tools, off-the-shelf mitigation methods for common harms and failure modes, and post-deployment monitoring platforms. Shared taxonomies and consistent definitions can support cross-team alignment, while functional documentation systems make responsible AI work internally discoverable, accessible, and actionable. 5. Progress: Track efforts holistically Sustaining support for and improving responsible AI practices requires teams to diligently measure and communicate the impact of related efforts. Tailored metrics and indicators can be used to help justify resources and promote internal accountability. Organizational and topical maturity models can also guide incremental improvement and institutionalization of responsible practices; meaningful transparency initiatives can help foster stakeholder trust and democratic engagement in AI governance." Miranda Bogen, Kevin Bankston, Ruchika Joshi, Beba Cibralic, PhD, Center for Democracy & Technology, Leverhulme Centre for the Future of Intelligence

AI is reshaping hiring – but can it truly support inclusion? From old-school walk-ins and newspaper ads to today’s platforms like LinkedIn, Naukri.com, and Indeed — the way we hire has evolved massively. Now, with AI stepping in, the challenge isn’t just speed or efficiency, but ensuring fairness and inclusion. Experts say AI can drive diversity if used carefully — - By reducing bias in job descriptions, - By spotting overlooked talent like career changers, and - By valuing soft skills such as empathy and adaptability. The future of hiring isn’t just about smart tech — it’s about smarter inclusion. So, how can organizations get this right? Here are 5 ways to make AI-powered hiring more inclusive: 1. Define your goals clearly – Know what inclusive hiring means for your team. 2. Use diverse data – AI is only as good as what you feed it. 3. Collaborate with tech partners – Demand transparency on bias testing. 4. Review results regularly – Spot patterns in who gets left out. 5. Keep humans in the loop – AI can screen, but people should decide. Technology is powerful, but inclusion needs intent. The question is: Are we using AI to make hiring truly fair? What inclusive hiring practices have you seen that actually work? #AI #Hiring #FutureOfWork #Inclusion #Leadership #Diversity

The Future of Talent Acquisition Isn’t What Most People Think. Every week I see another headline: “AI is coming for recruiters.” I think we’re asking the wrong question. AI isn’t replacing Talent Acquisition. It’s replacing the parts of Talent Acquisition that recruiters never really wanted to do in the first place. 🤖 Scheduling interviews. 🤖 Writing generic job adverts. 🤖 CV screening. 🤖 Interview notes. 🤖 Reporting. 🤖 Admin. That’s not where the real value of recruitment has ever lived. Over the next five years, I think we’ll see the biggest shift our profession has experienced since LinkedIn transformed sourcing. Recruiters won’t be judged by how many CVs they can review. They’ll be judged by whether they can answer questions like: ❓Should we even hire this role? ❓Could AI automate part of this job? ❓Can we redeploy someone internally instead? ❓What skills will this business need in two years’ time? ❓How do we assess potential rather than just experience? The recruiter of the future becomes less of a talent finder… …and more of a Talent Advisor. Job descriptions will evolve into skills profiles. Internal mobility will become just as important as external hiring. AI will make interviews more consistent, reduce bias and remove admin—but it won’t replace human judgement. And here’s what I think will separate great Talent Acquisition teams from average ones… Everyone will have access to similar AI tools. ❌ Not everyone will have trusted relationships with hiring managers. ❌ Not everyone will know how to influence a difficult stakeholder. ❌ Not everyone will know how to close exceptional talent. ❌ Not everyone will understand the commercial impact of a hiring decision. Technology will commoditise process. Human expertise becomes the differentiator. The recruiters who simply process vacancies should be worried. The recruiters who solve business problems, influence leaders and understand people have never had a bigger opportunity. The future of Talent Acquisition isn’t less human. It’s more human than ever. What’s one skill you think every recruiter will need by 2031?

✨ ‎ Let's be honest — recruiters spend way too much time perfecting search criteria when sourcing or reviewing candidates. Hours tweaking boolean strings, second-guessing qualifications, wondering if we're casting too wide (or too narrow) of a net... To save recruiters time, we’ve added AI-generated criteria and new presets. In Gem’s AI Sourcing and AI-powered App Review, you can now: → Build comprehensive qualification lists in seconds → Generate targeted search criteria based on your job post → Use popular presets to spot rising talent quickly → Review and edit AI-suggested criteria with ease Here's what makes this really special... We didn't just build this feature and ship it. We tested it extensively against user-generated search criteria. The results? Our AI-generated criteria performed as well or better than manually created searches across the board. Think about that for a second. The same (or better) quality candidates, without the hours spent perfecting search strings. This is what excites me about AI in recruiting — it's not about replacing human judgment. It's about giving recruiters back their time so they can focus on what matters most: building meaningful connections with candidates.

Someone built an AI agent that handles the entire job application loop. Not just writing the resume. The whole workflow: → finds roles → scores fit → tailors your CV → writes the cover letter → reviews the output → fixes layout → compiles the final PDF Most job tools stop at generation. This one is closer to an actual agent workflow. You upload your CV, documents, GitHub, and portfolio. Then the agent builds a structured profile of you. From there, it runs through commands: /setup → upload your documents → build your candidate profile /scrape → search multiple job portals → return jobs ranked by fit /apply job-url → score the role with reasoning → draft a tailored CV → write the cover letter → run a second agent review → revise the drafts → compile and verify the final PDF /expand → scan your GitHub and portfolio → find skills your CV missed /upskill → compare your profile against target roles → suggest a learning plan Built on Claude Code. Works for any country, any language. The interesting part is not “AI can write a resume.” That part is obvious now. The interesting part is the loop around it: → search → score → generate → review → revise → verify That is what makes agents useful. Not because they magically know the answer. But because they can keep running the workflow until the output is usable. Check the repo: https://lnkd.in/dFVmfJrY I'm Shrey Shah & I talk about harness engineering.

When AI hiring tools are instructed to be inclusive, outcomes improve. A recent study by Miles Yang, PhD found that doing so doubled the likelihood of selecting candidates with disabilities. (https://lnkd.in/ggTB8N3E) But when bias goes unchecked, here’s what can happen: “Their experience suggests they may be over 50 years old, which may impact their ability to adapt to new technologies and processes.” An HR executive shared that this exact output came from their AI-powered Applicant Tracking System (ATS). (https://lnkd.in/gGjQfS8k) Their reaction: As a lawyer, it violated age discrimination law. As an HR leader, it was unacceptable. And as someone over 50, it was infuriating. They immediately contacted the vendor, had the language removed, and pushed for guardrails to prevent it from happening again. AI can be a powerful tool, but it can also replicate and amplify bias. Let’s all look out for it. Escalate concerns to your HR or legal teams. Ask vendors to add safeguards to prevent bias. We can also push for explicit fairness instructions. In Yang’s study, they specifically instructed ChatGPT to “ensure that hiring decisions are grounded in merit and relevant qualifications, free from bias, and inclusive of all candidates” and “emphasize fair assessments that appreciate the unique perspectives and strengths brought by individuals from diverse backgrounds.” P.S. And, as I’ve shared in previous newsletters, we can question if AI is even helpful or needed for the task. — This is an excerpt from my upcoming “5 Ally Actions” newsletter. Subscribe and read the full edition at betterallies.com/subscribe #BetterAllies #BetterWorkplaces #AIHiring #AI #Inclusion #Fairness 🙏