Mathematics in Artificial Intelligence and Machine Learning Commission Members and Roles

Members:

• Chair: Professor Zakia Hammouch

• Secretary: Professor Fatima Zahrae Alaoui

• Treasurer: Professor Mohamed Zitane

• Central Africa Regional Representative: Professor Franklin Tchakounté (Cameroun)

• East Africa Regional Representative: Professor Fadhili M. Mrope (Tanzania)

• North Africa Regional Representative:

• Southern Africa Regional Representative: Professor Marica Moremdi (South Africa)

• West Africa Regional Representative: Professor Rodrique Kafando (Burkina Faso)

Roles:

1. Algorithm Development: Lead efforts in developing new algorithms for machine learning, data mining, and optimization, especially focusing on applications in big data and artificial intelligence.

2. Mathematical Foundations of AI: Work to clarify and expand the mathematical foundations of key AI and ML techniques, including areas like neural networks, reinforcement learning, and generative models.

3. AI for Social Good: Promote the use of AI and ML to address global challenges such as poverty, healthcare, education, and climate change, with a particular focus on problems unique to Africa.

4. AI in Healthcare: Facilitate research and applications of AI/ML for health diagnostics, personalized medicine, and epidemiology, especially in underserved or resource-limited environments.

5. AI Model Validation and Testing: Develop frameworks for rigorously testing and validating AI/ML models to ensure they are reliable, accurate, and free from bias.

6. AI in Governance and Policy: Advise on how AI/ML can be used in policymaking and governance, especially in data-driven decision-making for development goals.

7. Workforce Development in AI: Create initiatives to train a diverse AI workforce by promoting STEM education and offering career development programs, internships, and fellowships.

8. AI and Ethics Research: Investigate the ethical implications of AI, focusing on fairness, transparency, accountability, and the mitigation of algorithmic bias.

9. Mathematics in Natural Language Processing (NLP): Lead research in applying mathematical techniques to improve NLP models, addressing African language challenges and cross-lingual understanding.