Archaeology of Self: Reflexivity in Data Activism to Address Systemic Injustices

Abstract

Traditional data science education often neglects the importance of identity and sociopolitical context—especially for African American students whose lived experiences and cultural insights are essential for building justice centered technologies. This paper presents findings from the Data Activism Program, which integrated Dr. Yolanda Sealey-Ruiz’s Archaeology of Self™ framework to foster critical self-reflection and racial identity development among African American high school and college students. Through technical training in data science, art-based learning, and partnerships with social justice organizations, students engaged in reflexive practices that positioned them as active agents in challenging systemic oppression. Interviews reveal that the Archaeology of Self™ deepened students’ reflexivity skills and strengthened their sound racial identity, enabling them to interrogate bias within themselves and the data science process. We argue that embedding frameworks such as the Archaeology of Self™ into algorithmic design offers a concrete, transferable method for operationalizing reflexivity in data science and AI. This study contributes to the AI and data science community by offering actionable strategies to center identity and power in AI development.