AI Threats & Challenges
Navigating AI Pitfalls in Organizational and Social Contexts
Spot harm before it happens—build systems that account for context, not just code.
This course prepares cross-functional teams and decision-makers to anticipate the most common failures in AI implementation. Rather than focusing on technical flaws, it explores the ethical, operational, legal, and reputational risks that arise when automation outpaces oversight or when AI is applied without understanding real-world context.
Participants work through case-based examples that unpack issues like algorithmic discrimination, governance gaps, biased data pipelines, and trade-offs between efficiency and ethics. Each module equips learners to contribute to safer, more aligned AI projects—across product, HR, policy, or service functions.
Ideal for non-technical professionals influencing or implementing AI initiatives, this course strengthens organizational risk literacy and fosters the early-stage awareness needed to prevent scaled harm or public fallout.
Course Content
Module 1: Framing AI Risk in Organizational and Societal Contexts
Module 2: Algorithmic Discrimination, Harm, and Legal Exposure
Module 3: Poor AI Implementation and Systemic Consequences
Module 4: The Ethics–Efficiency Trade-Off in AI Design
Module 5: Internal Risk Mitigation: Building Guardrails Early
Module 6: Managing AI Across Functions: Aligning Risk Awareness
expected Time: 6-8 hours