Critical and Creative Data Literacies and Studies to Redress Mis-/Disinformation and Algorithmic Discrimination

We propose to address misinformation, disinformation, and algorithmic discrimination by developing an expansive interdisciplinary approach to critical data studies and data literacy. Our project will bring together groups that have been largely working in parallel, non-intersecting tracks: social-justice-oriented research and pedagogy in the arts and humanities and similarly focused computational work; Indigenous AI and intersectional technology developers; arts-based data literacy efforts and data science curricular development. Working together, we will: ? create mixed qualitative-quantitative tools to diagnose how misinformation and disinformation spreads; ? document how current forms of machine learning and recommender systems discriminate and build experimental methods for human-machine learning; ? build new data literacies by expanding whose voices matter and how they do through community-led data centers, public night schools and exhibitions, intersectional technology development workshops, and media projects to deter white nationalist radicalization; ? mainstream these approaches through media production, courses, small research grants, and workshops. This holistic approach is necessary because misinformation, disinformation, and algorithmic discrimination threaten so many aspects of public knowledge and higher education. By polluting the public sphere and amplifying conspiratorial thinking, they cut the threads entwining diverse cultures into a civilly engaged society. By automating discrimination through proprietary algorithms, they threaten to make the future repeat—rather than learn from—past mistakes; they jeopardize public knowledge by privatizing data and normalizing surveillance. To take on these fundamental challenges to truth, history, and knowledge, we therefore propose an approach that brings rich humanistic thinking about learning, history, narrative, and critique to bear on our data-filled world.