Ethical AI in HR: Navigating Bias & Fairness in Automation Training Course

 

Ethical AI in HR: Navigating Bias & Fairness in Automation Training Course

Introduction

As Artificial Intelligence (AI) rapidly integrates into core Human Resources functions, from talent acquisition to performance management, the critical imperative of ethical AI has never been more pronounced. This comprehensive training course is meticulously designed to equip HR professionals, HR technologists, and business leaders with the essential knowledge and practical strategies to navigate the complexities of AI bias, ensure fairness in automation, and uphold data privacy. Participants will delve into the nuanced ethical considerations that arise when AI systems influence human capital decisions, learning to identify, mitigate, and govern potential risks to maintain trust, foster equity, and ensure compliance within their organizations.

Embracing responsible AI in HR is fundamental for preserving organizational integrity and building a truly inclusive future of work. This program addresses the vital need for HR leaders to proactively develop robust AI governance frameworks, champion algorithmic fairness, and foster transparency in AI-driven HR solutions. By mastering the principles of ethical AI adoption, attendees will be empowered to design, implement, and monitor AI technologies that not only optimize efficiency but also align with core human values, thereby strengthening employee trust, reducing legal risks, and cementing the organization's reputation as a fair and forward-thinking employer.

Target Audience

  • HR Directors and Managers
  • HR Technologists and Analysts
  • Talent Acquisition Specialists
  • Diversity, Equity, and Inclusion (DEI) Leaders
  • Legal and Compliance Officers
  • Organizational Development Professionals
  • Business Leaders overseeing HR technology adoption

Duration

5 Days

Course Objectives

  1. Understand the core concepts of ethical AI and its specific implications for human resources.
  2. Identify potential sources and types of bias within AI algorithms used in HR processes.
  3. Learn strategies and techniques for mitigating algorithmic bias in HR automation.
  4. Navigate data privacy regulations and ensure ethical data handling in AI-driven HR.
  5. Develop frameworks for ensuring transparency and explainability in HR AI decisions.
  6. Formulate governance policies for the responsible design, deployment, and monitoring of HR AI.
  7. Assess the legal, reputational, and ethical risks associated with biased or unfair HR AI.
  8. Champion a culture of ethical AI adoption and continuous improvement within the organization.

Physical Training Schedule

Start & End Date

Location

Fee (USD)

Register

Aug 3- Aug 7, 2026

Nairobi

1,500

Aug 24- Aug 28, 2026

Pretoria

4,950

Sep 7- Sept 11, 2026

Nairobi

1,500

Sep 21- Sept 25, 2026

Mombasa

1,850

Oct 5- Oct 9, 2026

Nairobi

1,500

Oct 5- Oct 9, 2026

Kigali

2,950

Nov 2- Nov 6, 2026

Nairobi

1,500

Nov 23- Nov 27, 2026

Dar es Salam

2,950

Dec 7- Dec 11, 2026

Nairobi

1,500

Jan 5- Jan 9, 2027

Kigali

2,950

Jan 26- Jan 30, 2027

Mombasa

1,850

Feb 2- Feb 6, 2027

Nairobi

1,500

Feb 2- Feb 6, 2027

Pretoria

4,950

Mar 2- Mar 6, 2027

Nairobi

1,500

Mar 23- Mar 27, 2027

Dar es Salaam

2,950

Apr 6- Apr 10, 2027

Nairobi

1,500

Apr 20- Apr 24, 2027

Nairobi

1,500

May 4- May 8, 2027

Mombasa

1,850

May 25- May 29, 2027

Nairobi

1,500

Jun 1- Jun 5, 2027

Kigali

2,950

Jun 22- Jun 26, 2027

Nairobi

1,500

Jul 6- Jul 10, 2027

Dar es Salaam

2,950

Online Training Schedule

Start & End Date

Fee (USD)

Register

July 27 – July 31, 2026

800

Aug 3 – Aug 7, 2026

800

Aug 24 – Aug 28, 2026

800

Sept 7 – Sept 11, 2026

800

Sept 21– Sept 25, 2026

800

Oct 5 – Oct 9, 2026

800

Oct 26 – Oct 30, 2026

800

Nov 9 – Nov 13, 2026

800

Dec 7 – Dec 11, 2026

800

Jan 5- Jan 9, 2027

800

Jan 26- Jan 30, 2027

800

Feb 2- Feb 6, 2027

800

Feb 23- Feb 27, 2027

800

Mar 2- Mar 6, 2027

800

Mar 23- Mar 27, 2027

800

Apr 6- Apr 10, 2027

800

Apr 20- Apr 24, 2027

800

May 4- May 8, 2027

800

Jun 1- Jun 5, 2027

800

Jun 22- Jun 26, 2027

800

Jul 6- Jul 10, 2027

800

Related Courses