NYC School of Data 2020

The way data about people gets analyzed is socially outdated. Despite the political, cultural, and academic progress around the gender diversity discourse, the data related to identities such as non-binary, transgender, and intersex is often not included nor processed. The same goes with racial identity; the five common categories (African-American, Asian, Latino/Hispanic, White, First Nations) exclude a significant number of ethnicities and racial backgrounds, which is problematic when it comes to policy-making and distribution of resources based on data.

In this panel we will elaborate on implementing infrastructural modifications in data engineering and data analysis processes in order to maximize inclusivity, prevent marginalized individuals from falling through the cracks, and put an end to “data invisibility”.

We will also tackle these cutting-edge questions: what is data performativity? how governments could use data to target and opress certain identities? how can one turn personal data into a platform for social disobedience and non-violent means to protect unprivileged communities? how can you make theatre or performance art with data? how could data science be leveraged to analyze court documents and reveal systematic biases?

Join us for an exciting and unique panel on the intersection of Data Science and Performance Studies.
During our session participants are encouraged to develop new identification categories, ask questions, suggest strategies, and share experiences.

Keywords:
data performativity, identity, gender, ethnicity, race, data as bipower, anthropology of data, neurodiversity, physical diversity, disability, religion, immigration, data invisibility, data inclusivity, queer theory, social critical analysis