Publish Ready Outline AI communities in Africa and algorithmic pricing

Executive summary and transition plan

This outline organizes research on Vitamin D implications, the Deep Learning Indaba community, and algorithmic pricing research into a five section article for public release. The summary highlights key takeaways, cost and policy implications, community building outcomes, and regulatory risks from algorithmic pricing. Transition to the main sections by summarizing each section goal and linking to the methodology and references pages.

Audience

Target audience: Primary general tech readers with a secondary audience of academic researchers and policymakers.

Rationale: General tech readers will gain accessible context and policy implications. Academics will find citations and methodological notes in linked reference sections.

Deliverables and visualization plan

• Include data tables for study summaries and public cost estimates. Table types: Summary table and comparative table.
• Include visual charts to aid comprehension. Chart types: Bar charts for prevalence and cost, line charts for trends, choropleth map for regional Vitamin D status, and a simple network diagram for community growth.

Quoting policy

• Allow up to two verbatim quotes per researcher.
• Obtain written consent before publication.
• Attribute quotes with name, affiliation, and date.

Sources plan and placeholders

• Required sources: Collusion study 2019 [CollusionStudy2019].
• Collina and Arunachaleswaran 2024 [CollinaArunachaleswaran2024].
• Vitamin D meta analyses and WHO guidance [VitaminDMeta], WHO.
• Deep Learning Indaba materials and community reports [DeepLearningIndaba].
• Internal links: Methodology, Data appendix, Author bios.

Five section detailed outline

1. Executive summary and key takeaways
   • Key findings: One paragraph summary of cross cutting insights.
   • Implications: Quick bullets for policymakers and practitioners.
   • Transition: Guide links to each section and data appendix.
2. Vitamin D insights and public policy implications
   • Key findings: Prevalence, health outcomes, cost estimates.
   • Policy implications: Screening, supplementation strategy, cost benefit.
   • Limitations: Data gaps and confounders.
   • Next steps: Suggested trials and modelling.
3. Deep Learning Indaba and African AI ecosystems
   • Key findings: Community growth, capacity building, diversity metrics.
   • Implications: Talent pipelines, research networks, funding models.
   • Limitations: Coverage bias and resource constraints.
   • Next steps: Partnership recommendations and metrics.
4. Algorithmic pricing studies and regulation concerns
   • Key findings: Summary of 2019 collusion results and 2024 algorithmic pricing work.
   • Examples: How no swap regret algorithms change market prices with simple scenarios.
   • Implications: Competition policy, monitoring tools, algorithm audits.
   • Limitations: Model assumptions and external validity.
5. Implications and next steps for researchers and policymakers
   • Synthesis: Cross domain lessons and coordinated actions.
   • Policy roadmap: Immediate, medium and long term steps.
   • Research agenda: Data needs, reproducibility, and evaluation metrics.

Decision points and owners

• Audience confirmation: Owner – Author, Deadline – two days.
• Data and chart delivery: Owner – Data team, Deadline – five days.
• Quote permissions: Owner – Communications team, Deadline – ten days.
• Source verification: Owner – Research lead, Deadline – five days.