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Damon Chen

Conversational AI evangelism, education and careers

AI is rewriting how organisations hire and manage people — and without the right guardrails, innovation becomes exposure.

I help HR, Talent, and People leaders adopt AI safely, fairly, and legally. After a decade of independent practice, I am now the Head of Responsible AI and Industry Engagement at Warden AI, where we are building the global standard for independent AI auditing and assurance.

For nearly a decade I’ve supported the industry’s biggest shifts: starting with recruitment innovation (2015), building real AI/automation in practice (from 2019), and today leading AI governance & compliance so programmes stand up to regulators, clients and candidates.

What I deliver
• Governance & Compliance: EU AI Act readiness, ISO 42001 alignment, AI policy suites, bias & impact testing, board reporting.
• Readiness & Capability: Workforce AI diagnostics (genAssess) and leadership/team training (H.A.I.R.) to build safe, confident use.
• Evidence & Enablement: Procurement packs, audit trails, risk registers and practical guardrails embedded into HR workflows.

How I got here (one mission, five chapters)
• Recruitment is Awesome — recruitment operations automation (2015–2017)
• Luxatia / Global Conversational AI Summit — convening global leaders (2019–)
• PPLBOTS/Eunomia HR — boutique AI agency and governance & regulation advisory (2019–)
• genAssess — workforce AI readiness diagnostics (2024–)
• H.A.I.R. — training, content & community for responsible AI in HR (2022–)

If you want to move fast without breaking compliance, message me about a Governance Readiness Sprint or to book an exec briefing.

Trusted by HR/TA leaders and HR-tech vendors globally.
Testimonials: https://testimonial.to/martyn-redstone---ai-consulting-and-training-for-recruitment-and-hr/all

Martyn Redstone

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 👇)

Head of Responsible AI & Industry Engagement @ Warden AI | Ethical AI • AI Bias Audit • AI Policy • Workforce AI Literacy | UK • Europe • Middle East • Asia • ANZ • USA

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Martyn Redstone's LinkedIn Performance

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