# Practical ML for Limited/Low Resource Settings


## Introduction

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 breakthroughs 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.

The main goal of PML4LRS 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 contributons that highlight issues related to:

- Advances in algorithms and methods tailored for problems related with data-scarcity, imbalanced representations and limited computational resource
- 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.

## Topics

Resource constraints in developing countries can necessitate alternatives to conventional machine learning approaches. We invite submissions that address the following and related topic areas:

- Algorithms and Methods
  - Methods for collecting and generating training data within data scarce (limited labeled data) settings (such as weak labels, model-based pre-labeling, teacher-student models, and transfer learning).
  - Machine learning techniques applied to limited data (e.g. active learning, few-shot and zero-shot learning).
  - Approaches to training and inference on resource constrained devices (such as model quantization, model compression, model distillation, low precision training, model pruning methods, and generalized model optimizations).
  - Alternative learning methods coupled with deep models targeted for low resources settings.
  - Automated techniques to stratify and valuate data in order to increase throughput in low-resource settings.
  - Analyse models in the perspective of fairness, explainability, etc.

- Industry Experience and Applications
  - Data science and engineering practices that help balance accuracy/latency tradeoffs while scaling ML models in low resource environments.
  - Measuring success or impact that goes beyond algorithmic metrics (such as accuracy or F1 score).
  - Data-driven techniques that support public institutions (government transparency, healthcare, education etc).

- Social and Policy Topics
  - Successful ML solution implementation stories which work at a small scale (e.g. local institution, city) that could be applied at larger scale.
  - Connecting skilled professionals with the organizations that deeply understand the local problems.
  - Securing funding for proof-of-concept (POC) projects or for scaling existing POCs.
  - Building effective research and implementation teams, with a focus on challenges specific to developing regions such as countries in Africa.
  - When machine learning is NOT a viable option.
  - Strategies and policies enabling or enhancing AI/ML adoptions for developing countries.