The 4th Workshop on practical ML for Developing Countries: learning under limited/low resource settings

Girmaw Abebe Tadesse · Esube Bekele · Timnit Gebru · Matimba Shingange · Waheeda Saib · Luis Oala · Aisha Alaagib


The constant progress being made in machine learning needs to extend across borders if we are to democratize ML in developing countries. Adapting state-of-the-art (SOTA) methods to resource constrained environments such as developing countries can be challenging in practice. Recent breakthroughs in natural language processing and generative image models, for instance, rely on increasingly complex and large models that are pre-trained on large unlabeled datasets. In most developing countries, resource constraints make the adoption of these breakthrough challenges. Methods such as transfer learning will not fully solve the problem either due to bias in pre-training datasets that do not reflect environments in developing countries or the cost of fine-tuning larger models. This gap in resources between SOTA requirements and developing country capacities hinders a democratic development of machine learning methods and infrastructure.

Practical Machine Learning for Developing Countries (PML4DC) workshop is a full-day event that has been running regularly for the past 3 years at ICLR (past events include PML4DC 2020, PML4DC 2021 and PML4DC 2022). PML4DC aims to foster collaborations and build a cross-domain community by featuring invited talks, panel discussions, contributed presentations (oral and poster) and round-table mixers.

The main goal of PML4DC is to bring together researchers and practitioners (from academia, industry and government agencies) to reflect on aspects of designing, implementing, deploying and monitoring machine learning (ML) solutions that are typical in low resource environments across multiple sectors, such as healthcare, finance, agriculture, or education. Specifically, we encourage contributions that highlight issues related to:

-Advances in algorithms and methods tailored for problems related with data-scarcity, imbalanced representations and limited computational resources
-Industry practices to scale-up ML solutions in low resource settings while balancing performance and latency tradeoffs
-Societal and policy impacts of ML solutions in developing countries obtained via pilot studies, qualitative research, and human-in-the-loop settings.

Chat is not available.
Timezone: America/Los_Angeles