Ethical AI in HR: Navigating Challenges in Automation and Bias

Introduction to Ethical AI in HR

• Overview of AI applications in HR (e.g., recruitment, performance management, employee engagement).
• Understanding ethical concerns: fairness, privacy, and accountability.
• Case studies: Positive and negative examples of AI in HR.

Bias in AI: Causes and Consequences

• Types of biases in AI systems: Data bias, algorithmic bias, and user bias.
• How bias impacts HR decisions (e.g., hiring, promotions).
• Tools and techniques to identify and mitigate biases in HR systems.

Designing and Implementing Ethical AI in HR

• Principles of responsible AI design: Transparency, explainability, and inclusivity.
• Regulatory and legal considerations: Data protection, anti-discrimination laws.
• Best practices for AI implementation in HR workflows.

Building a Fair AI Ecosystem in HR

• Engaging stakeholders: HR teams, AI developers, and employees.
• Strategies to promote fairness and equity in AI outcomes.
• Developing a roadmap for continuous monitoring and improvement of AI systems.
• Interactive group exercise: Crafting an ethical AI policy for HR.

1ST BATCH: Tuesday, January 27, 2026 — Friday, January 30, 2026.

2ND BATCH: Tuesday, May 19, 2026 — Friday, May 22, 2026.

3RD BATCH: Tuesday, September 15, 2026 — Friday, September 18, 2026.

The training methodology integrates lectures, interactive discussions, collaborative group exercises, and
illustrative examples. Participants will acquire a blend of theoretical insights and hands-on practical
experience, emphasizing the application of learned techniques. This approach ensures that attendees return
to their professional environments equipped with both the competence and self-assurance to effectively
implement the acquired skills in their responsibilities.