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19 JAN | Online Training | Selecting talent through connection, not just evaluation

EXCLUSIVE & FREE ONLINE WORKSHOP FOR BEAUTY CLUSTER MEMBERS

📅 Date: Monday, January 19th, 2026
🕒 Time: 09:30 AM – 11:00 AM
👥 Format: Online (Zoom: https://us02web.zoom.us/j/84241253943)
🔐 Exclusive and free for Beauty Cluster members
🧠🗣️ Training led by: Virginia Carreras, Expert in AI, Automation & Gender Bias

Hiring Has Never Been This Strategic: AI, Regulation & Prompts That Truly Work in Recruitment

Hiring is evolving at a rapid pace. The arrival of AI, new European regulations, and clear evidence of bias in algorithms make it essential to rethink recruitment processes. In this 90-minute online workshop, you will discover how to apply AI in a responsible, efficient, and strategic way in recruitment, without losing human judgment and always aligned with business goals.

Key topics we will cover:

📌 AI Act: What really changes in recruitment
📌 Real biases in AI systems
📌 Research on real job offers in the beauty sector
📌 Checklist to create recruitment prompts that truly work

Includes:

🎯 Structured model for safe, non-discriminatory, and traceable prompting
🎯 Downloadable templates

What this workshop will help you improve:

  • Higher quality shortlists
  • Faster, measurable recruitment processes
  • Strong, data-backed arguments for hiring managers
  • Reduction of perceptual and digital bias
  • Clearer and more competitive job offers
  • Legal and reputational compliance

🎯 Target audience: Recruiters, HR/People teams, talent leaders, and department managers who want to:

✨ Use AI in an effective, legal, and human-centered way
✨ Make decisions based on data and qualitative observation
✨ Improve recruitment experience and efficiency
✨ Stand out in a market saturated with similar job offers

Discover how an efficient, bias-free recruitment process can transform not only who joins your organization, but also the culture that welcomes them.

Virginia Carreras – Founder of Smart Equality

Expert in AI, Automation & Gender Bias.