VOILA! season 2: Seminar #1

"Algorithmic Realism: Data Science Practices to Promote Social Justice"

Abstract:

Data scientists have confronted a gap between their desire to improve society and the harmful impacts of their creations. While algorithms once appeared to be valuable tools that advance social justice, today they appear to be dangerous tools that exacerbate inequality and oppression.

In this talk, Ben Green argues that improving society with algorithms requires transforming data science from a formal methodology focused on mathematical models into a practical methodology for addressing real-world social problems. The current approach to data science - which he calls “algorithmic formalism” - focuses on the formal mathematical attributes of algorithms. As a result, even when data scientists follow disciplinary standards of rigor, their attempts to improve society often entrench injustice.

In response to these limitations, he introduces “algorithmic realism,” a data science methodology that designs and evaluates algorithms with a focus on real-world impacts. Algorithmic realism expands the data science pipeline, providing concrete strategies for how data scientists can promote social justice. This new methodology also suggests more socially beneficial directions for the burgeoning fields of data science ethics, algorithmic fairness, and human-centered data science.

Panel discussion

The round-table discussion following the conference will address the theme of AI, Data Science and Social Justice, and will be moderated by researchers from Université Côte d'Azur. Pr. Green's presentation will be in English, and the round-table discussion in French.