Human-Centered AI

Background

Artificial intelligence permeates the fabric of human society, and its ability to generate data-driven insights and recommendations has potential benefits at an unprecedented scale, particularly for underserved and marginalized communities. Together with that promise, AI brings new risks: it can deepen existing inequalities, reinforce injustices, and create deeper disconnects within societies. Many of those risks stem from a growing gap between a technology-centric approach to building AI and the complex socio-cultural contexts the technology ends up embedded in.

This course takes a human-first approach instead. A human-first approach means creating AI systems where human perspective and needs drive the technological innovations throughout every stage of a system’s design: data collection, learning models, inference strategies, interaction paradigms, validation, deployment, evaluation, and maintenance.

Course Description

The course teaches future data scientists how to carry that human-centered approach through the decisions they make across the Data Science Pipeline. One question runs through the whole quarter:

How can we use design thinking to ensure data science and machine learning models are more transparent, approachable, and equitable?

Each week focuses on a specific stage of the pipeline, from use case development and the design phase onward, and pairs it with the human-centered topics and tools that belong at that stage. Students work through case studies and engage directly with the socio-technical questions every data scientist should be able to reason about when designing, building, and deploying AI systems.

The course also brings in outside voices on the themes that cut across the whole pipeline, including sessions on ethics, user-centered design, and safety.

Enrollment

The course is offered jointly as CSE 291A (Topics in AI) and DSC 266R, and is open to graduate students across computer science and data science.