In 2026, after nearly 90 years, the Institute of Design was folded into Illinois Tech's School of Design & Society.
This website is an archive of the impactful and inspirational work produced by the ID community before that transition.

László Moholy-Nagy opened the New Bauhaus in Chicago in 1937, treating design as a discipline that was teachable, testable, and consequential. Nine decades on, its scope had widened from the things people use to the systems they depend on: hospitals, government agencies, classrooms, supply chains, digital platforms. Along the way, ID community members developed the published research, methods, and frameworks that designers rely on every day.

Chief among them is human-centered design: close attention to how people actually live, and advocacy for what that research reveals. It shaped designers who are comfortable with complexity and ambiguity: curious, rigorous, inclusive, and stubbornly optimistic about design's ability to change the systems that shape our lives.

To every one of them, who taught, studied, built, critiqued, and stayed late in the studio: thank you, and keep designing.

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VICE cites Laura Forlano’s 2020 paper; “Participation Is Not a Design Fix for Machine Learning”

How ‘AI Isn’t Artificial or Intelligent’

December 31, 2022

ID Associate Professor Laura Forlano discusses how AI innovation is often powered by underpaid workers in foreign countries. At VICE, she explores the implications of utilizing human labor in lieu of responsible design: 

 

“
I think one of the mythologies around AI computing is that they actually work as intended. I think right now, what human labor is compensating for is essentially a lot of gaps in the way that the systems work.
—Laura Forlano, Associate Professor of Design

The article cites Forlano’s 2020 paper, “Participation Is Not a Design Fix for Machine Learning,” delving into how computer scientists are struggling to fix systemic issues with AI and critiquing human labor-based approaches to fill the gaps that machine learning cannot yet support.

“
One of the logical conclusions that a computer scientist might come to is that if you just add more and correct data to the systems that ultimately they will become better. But that in itself is a fallacy. No amount of data is going to fix the systems.
—Laura Forlano, Associate Professor of Design

Read more about the critique of AI and machine learning here. Read the paper Laura Forlano co-authored here.

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