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Women in STEM regularly experience harmful immediate and long-term consequences of gender bias. How can we find optimal ways to confront this gender bias, e.g. with the help of allies?

In this workshop led by computational neuroscientist Ida Momennejad, we will answer this question using agent based simulation, a method for implementing and testing long-term consequences of simple interaction rules, and Python to manipulate and test sexist interactions and their costs in an environment with a 20/80 gender ratio. We will then compare the long-term consequences of different strategies of bias confrontation. Ultimately, we will discuss how these simulations can be applied to institutional policies and future projects that make injustice visible and seek to offer balance.

Price: Free with suggested donation.

Materials: Participants are required to bring their own laptops with a functioning version of Python.

Audience: Open to anyone with a knowledge of Python.

Ida Momennejad is a cognitive computational neuroscientist at Columbia University. She studies how the brain builds and updates models of the world, or cognitive maps, for prediction and planning. Momennejad investigates algorithms with which the brain learns predictive representations and updates them via replay. She uses machine learning, reinforcement learning and neural networks, behavioral experiments (recently using virtual reality), fMRI, and electrophysiology in humans. In other work Ida uses graph theory and behavioral experiments on conversational networks to study collective memory. In her work on computational justice, Momennejad builds agent-based simulations with parameters derived from social psychology experiments. These simulations help demonstrate long-term consequences of bias propagation and identify effective strategies for bias confrontation.

About the Series
Computational Justice is a part of the Heart of the Machine program series responding to Refiguring Binaries curated by Kelani Nichole.