AI Literacy and Technical Mastery for Women in Media

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7 lessons

By Mwende Mukwanyaga

Overview
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This foundational course takes African journalists on an experiential journey into AI and gender. You begin by using AI tools, writing headlines, summarizing stories, before any theory is introduced. Then you interrogate: Who was represented? What biases emerged? Through a Master Tracker, you document observations across eight modules, building a cumulative case file.

Moving from utility to infrastructure to source, you learn structured testing, bias measurement, cost tracking, and how to interrogate AI as a hostile witness. The course culminates in a Pitch Lab, where you present evidence-based recommendations to peers and editors. You leave with practical tools, an action plan, and preparation for specialized tracks.

Curriculum

7 Sections
7 Lessons
Lifetime

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Module 1
Key topics in this module include: Course orientation and experiential arc introduction, What is AI? (Brief definitions/ light touch) + crash course on prompt engineering, Live Tool Lab: Participants use ChatGPT/Gemini/ NotebookLM/ any LLM to write a headline, summarize a story, suggest sources, Immediate reflection: What felt impressive? What felt off? What felt invisible? and Preview of Master Tracker
1
- 1.1
  AI as Utility: Use First
  6 Minutes
Module 2
Key Topics in this module include: What is bias? Defining bias in AI contexts, Where does bias come from? Gender as a lens: Introducing AWiM research on gendered inequalities in AI and newsrooms, How bias reflects in our work and Introducing the Master Tracker: How to track bias observations across the course.
1
- 2.1
  Interrogating AI: Adding the Gender and Bias Lens
  6 Minutes
Module 3
Key Topics in this module include: What is infrastructure? Defining infrastructure as the invisible systems that shape how things work, AI as political infrastructure, AI as socioeconomic infrastructure, AI as cultural infrastructure, AI as institutional infrastructure, “Ooh, that’s why this happened” and Adding infrastructure analysis to Master Tracker.
1
- 3.1
  Understand: AI as Infrastructure
Module 4
Key Topics in this module include: Why test systematically? What are we testing for? Structured testing methodologies, Bias Testing Checklist: Systematic tool for documenting test results and Live testing exercises
1
- 4.1
  From Intuition to Method
  7 Minutes
Module 5
Key Topics in this module include: The gap between testing and evidence, What is evidence in this context? From individual test to pattern, From pattern to meaning, Types of evidence for different audiences, Quantifying and qualifying evidence, aggregating participant evidence, From evidence to decision (preview of Module 7 and Preparing for Module 8 Pitch Lab:
1
- 5.1
  Measuring patterns/ Evidence That Matters in AI investigations
  6 Minutes
Module 6
Key topics in this module include: Why "hostile witness"? Why "source"? The hostile witness framework for AI, AI as a source of knowledge interrogated, AI as a source of power interrogated, AI as a source of inequality interrogated, AI as a source of safety and harm interrogated, AI as a source of narrative control interrogated, AI as a source of possibility interrogated with hope, Revisiting evidence from Module 5 through interrogation and Preparing for Module 7 (Trackers) and Module 8 (Pitch).
1
- 6.1
  Re-imagine: AI as a Source/ hostile witness
  5 Minutes
Module 7
Key Topics in this module include: Why trackers? The Bias Tracker for policy and accountability, The Cost Tracker for tooling and resource decisions and Integrating trackers with the Master Tracker:
1
- 7.1
  Act: Trackers for Decision-Making ( Bias, Cost, and Policy)
  5 Minutes

Mwende Mukwanyaga

Mwende (Olive) Mukwanyaga is a Nairobi-based Gender & AI Systems Consultant and Media Development & AI Safety Specialist working at the intersection of journalism, gender equity, and AI governance. She specializes in auditing AI systems for bias, designing safety and accountability frameworks, and supporting newsrooms to adopt AI responsibly in high-risk contexts such as elections, conflict reporting and technology-facilitated gender-based violence (TFGBV). Mwende is Co-Founder of AI Salon and co-creator of the AI Newsroom Adoption MethodKit with DW Akademie. Her work bridges technical experimentation, institutional strategy, and training to advance ethical, gender-sensitive AI across African media ecosystems.

AI Literacy and Technical Mastery for Women in Media

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Mwende Mukwanyaga

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AI Literacy and Technical Mastery for Women in Media

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Mwende Mukwanyaga

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