Data science has tremendous potential to inspire positive outcomes in the world. However, there are concerns about the ethical deployment of data science techniques and the ways that the effects of data science may reify inequities and biases.
The promotion of data science requires a dedicated understanding of the power within societies and knowledge communities to ameliorate negative, unjust effects. Data science is and will continue to restructure multiple aspects of our world and it is important to maintain a commitment to questions of power, inequity, responsibility, surveillance, justice, and harm to ensure that collecting, manipulating, storing, visualizing, learning from, and extracting useful information from data is done in a reproducible, fair, and ethical way.
Felix Cheung (Department of Psychology, Faculty of Arts and Science, University of Toronto): “A New Standard of Equality-Focused Analysis in Well-being Science.”
David Nieborg (Department of Arts, Culture, and Media, University of Toronto Scarborough, University of Toronto): “Tracking global data flows in the app ecosystem.”
Amaya Perez-Brumer (Dalla Lana School of Public Health, University of Toronto): “Towards Data Justice: Peruvian Transgender Women-Led HIV Science Data Governance.”
Robert Soden (Department of Computer Science, Faculty of Arts and Science, University of Toronto): “Building Equity into Climate and Disaster Risk Models in the Himalayas.”