Inequities can arise from almost any aspect of society, including climate change, the implementation of machine learning, mobile technologies and genomics in health care and daily life, the design and spatial configuration of cities, as well as social and economic policy.  Consider, for example, the COVID-19 pandemic that has profoundly and disproportionately affected marginalized communities, the elderly and women. This has unveiled massive social, economic, health and political inequalities on an unprecedented scale, shining a light on inequities that have long existed and worsened over time. This disproportionate impact has led to reactive policy decisions based on limited evidence.  

Drawing on the disciplinary strength inequity studies at U of T and recognizing that data-based technologies can potentially increase inequities, this theme encourages the generation of evidence (data and inference) and tools that enhance our understanding of inequity while supporting equitable social change. 

The availability of new, heterogeneous data has transformed traditional disciplines, encouraging researchers to address pressing questions in innovative, data-driven ways. The opportunity to link these newer resources with socioeconomic and demographic data, and/or to co-develop research projects with under-resourced communities, extends the breadth of possible investigations to understand and act upon social inequities in service of empowering the communities they affect.