As organizations rush to adopt Artificial Intelligence, the gap between innovation and governance is widening. AI brings transformative power, but it also introduces significant risks regarding data privacy, algorithmic bias, and legal liability.
This course moves beyond the theoretical philosophy of ethics and provides a practical framework for Operationalizing Responsible AI. We guide your cross-functional teams—from legal to engineering—on how to innovate safely. We cover the critical intersection of technical implementation and the evolving regulatory landscape in Asia (including China’s PIPL) and the West (GDPR/EU AI Act).
This training ensures your organization can leverage Generative AI and Machine Learning without compromising brand reputation, customer trust, or legal compliance.
Who Should Attend?
This course is essential for stakeholders involved in the data lifecycle and risk management:
Legal & Compliance Officers: Navigating the complex web of AI and data privacy regulations.
Data Scientists & AI Engineers: Learning to build models that are explainable, fair, and unbiased.
HR Directors: Managing the risks of AI in recruitment and employee analytics.
C-Suite & Risk Managers: Understanding organizational liability and establishing governance boards.
IT Security Teams: Preventing data leakage through public Generative AI tools (e.g., ChatGPT).
Detailed Learning Objectives
By the end of this training, participants will be able to:
Operationalize AI Ethics: Translate abstract concepts like “Fairness” and “Transparency” into technical requirements and business processes.
Navigate Global & Regional Law: Understand the compliance requirements of the GDPR (Europe), PIPL (China), and local data protection laws in Taiwan and Asia.
Mitigate Algorithmic Bias: Identify, measure, and correct bias in training datasets to prevent discriminatory outcomes in hiring, lending, or customer service.
Govern Generative AI: Establish policies for the safe use of LLMs (Large Language Models) to prevent intellectual property theft and data leaks.
Implement “Human-in-the-Loop”: Design workflows where human oversight effectively validates AI decisions in high-stakes scenarios.
Course Curriculum (Sample Modules)
Module 1: The Pillars of Responsible AI
Defining FAT: Fairness, Accountability, and Transparency.
The “Black Box” Problem: Why Explainable AI (XAI) matters for business trust.
Case Studies: Real-world examples of AI implementation failures and reputational damage.
Module 2: The Regulatory Landscape (Asia & Global)
GDPR vs. PIPL: Navigating the differences between European and Chinese data regulations.
The EU AI Act: Risk categorization and what it means for Asian exporters.
Intellectual Property (IP) risks in Generative AI output.
Module 3: Data Privacy & Security in the AI Era
The “Shadow AI” Risk: Managing employees using unauthorized AI tools.
Techniques for data anonymization and differential privacy.
Secure prompting: How to use tools like ChatGPT without leaking trade secrets.
Module 4: Detecting & Mitigating Bias
Types of Bias: Historical, Sampling, and Proxy bias.
Auditing datasets and models for fairness.
Building diverse teams to spot blind spots in AI development.
Module 5: Building an AI Governance Framework
Establishing an AI Ethics Committee or Review Board.
Creating an AI Acceptable Use Policy (AUP).
Conducting Algorithmic Impact Assessments (AIA) before deployment.
Why Choose Ultimahub?
Bridging the Gap: We speak both “Code” and “Law.” We facilitate the crucial dialogue between your technical teams and your legal/compliance departments.
Regional Expertise: We specifically address the challenges of operating in the Asian market, including the strict requirements of China’s Personal Information Protection Law (PIPL).
Practical Toolkit: Participants leave with templates for AI Impact Assessments and draft governance policies suitable for immediate use.
Delivery Format
On-Site Workshop: Available throughout APAC, and globally.
Virtual Live Training: Interactive webinar style suited for distributed regional teams.
Hybrid Delivery: A mix of self-paced e-learning for foundational knowledge and live workshops for case study analysis.
