Practical ML for Developing Countries: learning under limited/low resource scenarios

The constant progress being made in artificial intelligence needs to extend across borders if we are to democratize AI in developing countries. Adapting the state-of-the-art (SOTA) methods to resource constrained environments such as developing countries is challenging in practice. Recent breakthroughs in natural language processing (NLP), for instance, rely on increasingly complex and large models (e.g. most models based on transformers such as BERT, VilBERT, ALBERT, and GPT-2) that are pre-trained in on large corpus of unlabeled data. In most developing countries, low/limited resources means hard path towards adoption of these breakthroughs. Methods such as transfer learning will not fully solve the problem either due to bias in pre-training datasets that do not reflect real test cases in developing countries as well as the prohibitive cost of fine-tuning these large models. Recent progress with focus given to ML for social good has the potential to alleviate the problem in part. However, the themes in such workshops are usually application driven such as ML for healthcare and for education, and less attention is given to practical aspects as it relates to developing countries in implementing these solutions in low or limited resource scenarios. This, in turn, hinders the democratization of AI in developing countries. As a result, we aim to fill the gap by bringing together researchers, experts, policy makers and related stakeholders under the umbrella of practical ML for developing countries. The workshop is geared towards fostering collaborations and soliciting submissions under the broader theme of practical aspects of implementing machine learning (ML) solutions for problems in developing countries. We specifically encourage contributions that highlight challenges of learning under limited or low resource environments that are typical in developing countries.