Participants will be able to: - Assess organisational risks related to AI adoption, including algorithmic bias, opacity, labour displacement, and technology-facilitated gender-based violence - Identify how AI systems may reproduce gender inequality in hiring, promotion, content generation, and audience targeting — grounded in empirical research from African media contexts - Develop a newsroom-specific Gender-AI Integration Roadmap using structured diagnostic tools and governance templates - Establish governance mechanisms for ethical, transparent, and accountable AI use, informed by international frameworks and locally grounded standards - Design reskilling and leadership pathways to ensure women’s meaningful participation in AI decision-making at editorial and institutional levels

The course combines multiple pedagogical modes to maximise practical application and peer learning: • Self-paced modules with short video lectures, practical guides, and structured readings drawn from current AI governance literature and African media research • Real-world case studies from African and Global South contexts, including Quanta’s own research on digital media ecosystems, information manipulation, and climate discourse across the continent • Interactive templates and policy drafting exercises designed for immediate institutional application • Weekly live sessions focused on peer learning, organisational problem-solving, and expert Q&A • Capstone project: a draft AI governance framework tailored to each participant’s newsroom, reviewed by Quanta’s team of data scientists, media scholars, and governance specialists

• Gender bias in automated content generation, recommendation algorithms, and analytics pipelines • Technology-Facilitated Gender-Based Violence (TFGBV) risks in AI-mediated newsroom environments • Case studies from African media contexts, including computational analysis of gendered discourse patterns in Southern African news media • Practical session: Auditing an AI tool for gender bias using structured evaluation frameworks

• Drafting internal AI governance policies: accountability structures, transparency requirements, and ethical standards • Audit and accountability mechanisms: how to evaluate AI procurement, vendor claims, and algorithmic outputs • Ethical procurement and vendor assessment using structured evaluation guides • Drawing on Quanta Intelligence’s experience developing the University of Eswatini’s Generative AI Guidelines as a governance case study

• Gender-AI Integration Roadmap development: participants draft tailored roadmaps for their newsrooms • Reskilling and workforce transition strategies to ensure equitable AI adoption • Institutional oversight structures: decision committees, review boards, and reporting mechanisms • Capstone presentation: Participants present draft AI governance frameworks for peer review and expert feedback