Towards a Model of Trauma-Informed AI Education

Abstract: The field of Data Science has seen rapid growth over the past two decades, with a high demand for people with skills in data analytics, programming, statistics, and ability to visualize, predict from, and otherwise make sense of data. Alongside the rise of various artificial intelligence (AI) and machine learning (ML) applications, we have also witnessed egregious algorithmic biases and harms -- from discriminatory outputs of models to reinforcing normative ideals about beauty, gender, race, class, etc. These harms range from more high profile cases such as the racial bias embedded in the COMPAS recidivism algorithm, to more insidious cases of algorithmic harm that compound over time with retraumatizing effects. This dissertation takes a closer look at how current efforts attempt to integrate AI ethics into Data Science curricula -- as well as the cases of harm that may be missing from larger conversations on responsible AI. This includes retraumatizing effects of recommender systems, social media content organization and the struggle for visibility, and discriminatory content moderation of marginalized individuals. The empirical work of this dissertation offers insight into these lesser discussed algorithmic harms, as well as interventions for how to train engineers with trauma-informed methods. While the world grapples with policy, standards, and guidance for AI ethics, this work specifically offers some additional methods for fostering ethical foresight and preventative care in AI contexts. Finally, I contribute a framework for trauma-informed AI education -- inspired from public health initiatives and multiple points of intervention for systemic change. The framework centers ongoing and preemptive efforts to promote the welfare of communities and vulnerable stakeholders, while centering the most impacted by algorithmic harm.

Supervisory Committee

Chair: Emma Spiro, Associate Professor, University of Washington Information School
GSR: Amy Zhang, Assistant Professor, University of Washington Allen School of Computer Science & Engineering
Member: Jevin West, Associate Professor, University of Washington Information School
Member: Wanda Pratt, Associate Professor, University of Washington Information School