Core Modules 0 – 6

• Define AI in plain language and distinguish it from other software
• Identify the three types of AI encountered in professional settings (Generative, Predictive, Computer Vision)
• Name at least five things AI cannot do, regardless of how confident it sounds
• Spot one AI system in their own workplace and classify it by type

What makes AI different from other software? What can generative AI not do — regardless of how confident it sounds? Why does it hallucinate sources and fabricate statistics?

• Explain how generative AI produces output without "knowing" anything
• Identify the key failure modes of large language models
• Spot hallucination signals in a real document (a national policy draft, South Africa)

AI is trained on data. That data reflects the world's inequalities. Understanding how AI learns is the first step to spotting when it's failing people — including you.

• Describe how AI learns from historical data in non-technical terms
• Explain why training data reproduces systemic bias
• Identify proxy discrimination in a real AI credit-scoring scenario

Discrimination can happen without discriminatory intent. In Ghana, AI trained on historical data carries the weight of colonial-era economic exclusion and structural inequality forward — often invisibly.

• Define algorithmic bias in plain language for a non-technical audience
• Analyse an AI recommendation for disparate impact on a protected group
• Apply an equity lens to AI deployment decisions in their sector

Verification is a professional skill, not a technical one. How do you decide when AI output is good enough to act on — and when it needs a second look?

• Apply the Funda Five™ Trust step to any AI output
• Identify deepfake and synthetic content signals in audio and video
• Build a personal verification checklist relevant to their role

"The algorithm decided" is not a legal or ethical defence under Ghanaian law. When AI affects someone's employment, credit, or healthcare, a person in an organisation is accountable.

• Map the accountability chain for an AI decision in their own organisation
• Identify the Labour Act, 2003 (Act 651) exposure from AI recruitment tools
• Raise a substantive AI concern through the right organisational channels

The question is never "AI yes or no?" It's "AI for what purpose, for whom, in what context, with what oversight?" This module synthesises the entire course through two contrasting cases.

• Evaluate an AI deployment across all five Funda Five™ dimensions
• Write a professional AI judgment statement suitable for a team briefing
• Articulate when AI deployment is and isn't appropriate in their sector