About

Dr Abdoul Jalil Djiberou Mahamadou is a postdoctoral scholar in AI ethics at the Stanford Centre for Biomedical Ethics. He is part of the inaugural cohort of the Stanford-GSK.ai fellowship on AI’s ethical, legal, and social implications in healthcare. He’s passionate about developing technologies that benefit everyone, specifically, socio-technical, context and culturally sensitive AI risk evaluation frameworks. At Stanford, his research focuses on algorithmic bias and fairness, stakeholder engagement in AI risk assessment at Stanford Health Care, and global health. Before joining Stanford, he completed a postdoctoral fellowship in AI and cognitive aging at Simon Fraser University, where he studied the association between lifestyle activities and cognitive health. He also undertook a short postdoctoral fellowship at Clermont Auvergne University, where he contributed to the development of an explainable algorithm for anomaly and concept drift detection in stream data. Dr Djiberou hold a PhD in Computer Science, an MSc in Computer Science, an MEng in Applied Mathematics from Clermont Auvergne University, and a BSc in Applied Mathematics from Sidi Mohamed Ben Abdellah University. His academic journey has been recognised with several prestigious fellowships, awards, and distinctions, including the Mitacs Accelerate Postdoctoral Fellowship and recognition as one of the top-performing Nigerien students in France in 2019.

Read more about his journey in his CV.

News

• October 2025: My paper “Reframing Justice in Healthcare AI: An Ubuntu-Based Approach for Africa” is now available online in the Developing World Bioethics journal.
• September 2025: My paper “Reframing Justice in Healthcare AI: An Ubuntu-Based Approach for Africa” is accepted for publication in the Developing World Bioethics journal.
• July 2025: My paper “Algorithmic Fairness Beyond Legally Protected Groups and When Group Labels Are Unknown” is accepted for presentation at the AI Ethics and Society (AIES) Conference.
• June 2025: I have an accepted abstract “On Defining “Vulnerable Groups” In AI Bias Mitigation Solutions” and panel “Staying One Step Ahead: Emerging AI, Eroding Ethical Principles, and the Impact on Clinical AI Deployment” at the 27th American Society for Bioethics and Humanities Annual Conference.
• May 2025: My paper Interpretable machine learning for precision cognitive aging is published in the Frontiers in Computational Neuroscience journal.
• Feb 2025: I am excited to share that my paper Revisiting Technical Algorithmic Fairness and The Impact of Lifestyle Factors on Trajectories of Cognitive Subtypes in the Older Adult Population are accepted for publication respectively in the Annual Review of Biomedical Data Science and Nature Scientific Reports journals.
• Dec 2024: I presented my work on revisiting technical algorithmic fairness and participatory AI at the NeurIPS Algorithmic Fairness Through the Lens of Metrics and Evaluation Workshop.
• Sep 2024: I presented my work on algorithmic fairness at the 26th American Society for Bioethics and Humanities Annual Conference.
• Oct 2023: I co-presented a panel on “Artificial intelligence for precision medicine: How close to ‘personalized’ can we really get?” at the 6th Ethical, Legal, and Social Implications of Genomic Research Annual Meeting.
• June 2023: I presented my work on AI and Cognitive Aging at the Organization for Human Brain Mapping Annual Meeting.
• June 2023: I started my postdoctoral fellowship in AI and Biomedical Ethics at the Stanford Centre for Biomedical Ethics.

Personal Projects

• LLM Water Tracker: I attended a seminar at Stanford on responsible AI during which a panellist made a call on the awareness of LLMs’ environmental impact, notably, the cost of inference on water usage. I spent a weekend developing LLM Water Tracker, a Google Chrome Extension that tracks LLM inference water usage equivalence across web LLM platforms, including chatgpt.com, gemini.google.com, and claude.ai. This tool aims to help people make environmentally conscious LLM use.
• ZarmaTrad: My mother tongue is Zarma, a dialect spoken in Niger and Mali. It is an oral-only language and is under-resourced. On the Internet, we could find only dictionaries from almost twenty years ago in English, French, and Zarma. Our goal is to develop an AI machine translation model for this language.

Press

• A piece in BioSpace about data diversity in AI-driven drug development.
• A piece about my research and the potential of AI for Niger by a national news outlet.

Teaching

I enjoy teaching and have accumulated over 290 hours of teaching experience on diverse topics ranging from bioethics, research ethics, and AI.

Stanford University, Stanford, California, USA - +34 hours

• Foundation of Bioethics, Winter 2024, Winter, Spring, Fall 2025
• Responsible Conduct of Research, Fall 2023, Autumn 2024, Spring 2025

African Development University, Niamey, Niger - 30 hours

• Introduction to Machine Learning, Summer 2022

University of Clermont Auvergne, Aubiere, Auvergne-Rhone Alpe, France - 184 hours

• Relational Databases, Spring 2020
• Statistics and Probability, Fall 2020
• Information Systems, Fall 2020
• Databases and Web, Fall 2020
• Relational Databases, Spring 2019
• Algorithmic, Spring 2019
• Software Engineering, Spring 2019
• Shell Programming, Spring 2018

Selected Publications and Presentations (Google Scholar Profile)

1. Ochasi, A., Mahamadou, A. J. D., Altman, R., Nkwocha, L.U.C. (2025). Reframing Justice in Healthcare AI: An Ubuntu-Based Approach for Africa. Developing World Bioethics. https://doi.org/10.1111/dewb.70007.
2. Mahamadou, A. J. D., Rodrigues, E. A., Vakorin, V., Antoine, V., & Moreno, S. (2025). Interpretable Machine Learning for Precision Cognitive Aging. Frontiers in Computational Neuroscience, 19, 1560064.
3. Mahamadou, A. J. D., & Trotsyuk, A. A. (2025). Revisiting technical bias mitigation strategies. Annual Review of Biomedical Data Science, 8.
4. Mahamadou, A. J. D., Lea Goetz, Russ B. Altman. (2024). Individual Fairness Through Reweighting and Tuning. ArXiv:2405.01711.
5. Mahamadou, A. J. D., Emma A. Rodrigues*, Vasily Vakorin, Sylvain Moreno. (2023). Lifestyle Factors and Cognitive Aging: A Personalized Approach. Organization for Human Brain Mapping Annual Meeting.
6. Anne Marthe Sophie Ngo, Mahamadou, A. J. D., Michael F. Mbouopda, Engelbert MN. (2022). DragStream: An anomaly and concept drift detector in data streams. International Conference on Data Mining IncrLearn Workshop.
7. Emma A. Rodrigues, Mahamadou, A. J. D., Violaine Antoine, Sylvain Moreno. (2022). Profiling the Healthy Aging Population: A Machine Learning Approach. Cognitive Neuroscience Society Annual Meeting.
8. Nicolas Kerckhove et al. (2021). eDOL mHealth App and Web Platform for Self-monitoring and Medical Follow-up of Patients With Chronic Pain: Observational Feasibility Study. Journal of Medical Internet Research.
9. Mahamadou, A. J. D., Antoine Violaine, Nguifo Engelbert Mephu, Moreno Sylvain. (2021). Apport de l’entropie pour les c-moyennes floues sur des données catégorielles. Revue des Nouvelles Technologies de l’Information.
10. Mahamadou, A. J. D., Antoine Violaine, Nguifo Engelbert, Moreno Sylvain. (2020). Categorical fuzzy entropy c-means. IEEE International Conference on Fuzzy Systems.
11. Mahamadou, A. J. D., Antoine Violaine, Christie Gregory, Moreno Sylvain. (2019). Evidential clustering for categorical data. International Conference on Fuzzy Systems.