Good Systems has awarded 20 summer fellowships to faculty, researchers, and graduate students from 13 departments and units across UT Austin to advance the mission of this research grand challenge by funding innovative research that investigates the ethical implications of AI technologies in society.

Data and integrated technologies can transform cities. Sensors and artificial intelligence can assist people crossing busy streets, provide broadband access at bus stops, or use drones that use AI to help fight fires.

Good Systems announces three summer research opportunities to help advance its mission to design ethical AI technologies to benefit society. Learn more about Faculty Fellowships, Graduate Student Fellowships, and Undergraduate Research Experiences.

UT researchers Maria De-Arteaga and Min Kyung Lee talk about their different but complementary work to make algorithms less biased and harmful.

S. Craig Watkins looks beyond algorithm bias to an AI future where models more effectively deal with systemic inequality.

The productive working relationship between the University of Texas and the City of Austin can serve as a framework for other governments interested in improving their communities with technology.

AUSTIN (KXAN) — Austin City Council members took a step Thursday to loosen the restrictions on Accessory Dwelling Units (ADUs) and make them more affordable.

Computer Science Professor Peter Stone and School of Information Assistant Professor Elliott Hauser share about their new Good Systems project "Living and Working with Robots" and why it's important to design robotic technology in the real world.

The situation is dire for potential homeowners in Austin making less than the $80,000 annual median household income. Rapid population growth coupled with massive migration into the city have caused the demand for homes to soar — and along with it, prices.

Among the principles for ethical and responsible AI none is more prominent than the “fairness and non-discrimination” principle, that is, the idea that data-based systems like AI/ML should avoid unjust impacts on people, especially those from historically marginalized populations.