ConfSync

CONFERENCE

HR &
Operations
#15

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Case study

Scenario: When AI Meets HR

Your company has implemented a new AI-powered system to support HR functions: resume screening, performance tracking, and employee engagement analytics. The system promises speed, objectivity, and cost savings — but HR staff and employees are raising red flags.

Concerns range from algorithmic bias, data transparency, and loss of human judgment, to the impact on diverse employees, especially those from underrepresented or non-traditional backgrounds. HR must now evaluate the risks, navigate ethical implications, and decide how to move forward — without losing trust.

Meet the Team

Layla (27, Arab, Jordanian, Gen Z – Junior Data Analyst)
  • Applied through a coding bootcamp; lacks a university degree.
  • Concerned that the resume screening algorithm filters her out unfairly.
  • Frustrated by automated rejections and the lack of human feedback.
Marcus (36, Black, American, Millennial – HR Business Partner)
  • Worried about AI bias — especially in resume filtering and performance reviews.
  • Knows systems trained on biased data often disadvantage minority candidates.
  • Advocates for human oversight but meets resistance from leadership.
Hiroshi (48, East Asian, Japanese, Gen X – Engineering Manager)
  • Excited about automation and data-driven insights.
  • Believes AI makes decisions more objective.
  • Cautious about how AI interprets behavioral patterns like engagement or leadership presence.
Santiago (58, Latino, Argentinian, Baby Boomer – Recruiter)
  • Feels replaced — his experience and intuition no longer valued.
  • Believes decisions are becoming robotic, and less human-centered.

Lead the discussion

⚫ Round 1: Step into Their Shoes

  1. How is this person affected by AI?
  2. What unique risks are they facing (due to culture, age, neurodiversity, education path, etc.)?
  3. How would you support them as an HR leader?

🔵 Round 2: Where the System Falls Short

  1. How might AI reinforce existing inequalities?
  2. What happens when performance or engagement data is interpreted without context?
  3. Who’s accountable when an algorithm makes a bad call?

🟢 Round 3: Building Ethical Guardrails

  1. What decisions should never be fully automated?
  2. How can we audit AI tools for fairness, bias, and transparency?
  3. Should employees have a right to appeal AI-based decisions?

🟠 Round 4: Future-Proofing HR + AI

  1. What questions should HR ask when adopting any new AI system?
  2. How can you ensure AI works across global, cultural, and communication styles?
  3. How can we train HR teams to critically evaluate tech, not just trust it?

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