AI Ethics • ML Fairness • ML Transparency • Algorithmic Literacy • CS Ethics Education • ML Auditing

Jessie J. Smith (Jess) is a PhD candidate and a Google PhD Fellow at The University of Colorado Boulder researching machine learning and AI ethics with an emphasis on algorithmic fairness and transparency. 

Previous Grad School Internships:

• Spotify's Algorithmic Impact Team (Summer 2022)
• Apple's Machine Learning Robustness Team (Spring 2022)
• Microsoft's FATE (Fairness, Accountability, Transparency, and Ethics in AI) Team (Summer 2021)

Jess' dissertation focuses on operationalizing fairness within machine learning systems, with a focus on the application of recommender systems---which includes developing best practices to empirically measure the inherently unobservable and contested construct of fairness. Her previous work has explored ways to improve algorithmic literacy, trust, and agency in 'multistakeholder' systems, especially when values come into conflict with one another [read more about this work].

Jess also does part-time consulting with small to large size tech companies to work with data scientists on incorporating fairness, transparency, and/or auditing into the machine learning and AI lifecycle. This includes working with employees across various teams to understand how fairness can most appropriately be encoded into algorithms and processes while aligning with an organization's values.

Jess is the co-host and co-founder of The Radical AI Podcast - discussing the most pressing issues of AI and society with world-renowned experts such as Ruha Benjamin and Timnit Gebru. 

Jess was previously a graduate instructor for introductory Computer Science (in Python) at CU Boulder and helped restructure the curriculum to include more case studies and ethical speculation for the unintended consequences of technology. Check out Jess' open source repo for intro coding courses that includes 5 modules for teaching ethics and social science through the lens of coding!

Jess is co-advised Dr. Casey Fiesler and Dr. Robin Burke. She is a member of the Internet Rules Lab and That Recommender Systems Lab.