Participatory Machine Learning Using Community-Based System Dynamics

The pervasive digitization of health data, aided with advancements in machine learning (ML) techniques, has triggered an exponential growth in the research and development of ML applications in health, especially in areas such as drug discovery, clinical diagnosis, and public health. A growing body of research has shown evidence that ML techniques, if unchecked, have the potential to propagate and amplify existing forms of discrimination in society, which may undermine people's human rights to health and to be free from discrimination. We argue for a participatory approach that will enable ML-based interventions to address these risks early in the process and to safeguard the rights of the communities they will affect. The promise of machine learning is its ability to efficiently comb through data to find valuable patterns and insights that the machine may use to make predictions or to aid humans in making decisions. However, data reflect numerous societal and human biases that shape their generation, availability, collection, synthesis, and analysis.