Positionality is the social and political context that influences, and potentially biases, a person's unique but partial understanding of the world. Machine learning (ML) systems have positionality too, which is embedded through choices we make when developing classification systems and datasets. In this tutorial, we uncover positionality in ML systems with a focus on the design of classification systems, study the impact of embedded positionality, and discuss potential intervention mechanisms.

Trevor Deley IBM CAS, University of Ottawa

Emanuel Moss CUNY Graduate Center / Data & Society Research Institute

Christine Kaeser-Chen Google Research

Elizabeth Dubois University of Ottawa

Friederike Schüür Cityblock Health

Our tutorial was part of the ACM FAT* 2020 Conference (full conference program). Thank you for joining us at the conference!

If you want to find out more, here are the slides and 3 case studies used in the tutorial:

• Link to Slides
• Link to 3 Case Studies: Facial recognition for travel and border control; Health records for risk prediction; Remote sensing for land use survey.

If you have comments, questions, ideas, or follow-ups, we'd love to hear from you!

Contact us at data-archaeology@googlegroups.com!