Complex Systems and UM School of Information Faculty Abigail Jacobs, who studies the assumptions in machine learning systems, is one of the 2024 Microsoft Research AI & Society Fellows.

From the program website:

The program 'aims to catalyze research collaboration between Microsoft Research and eminent scholars and experts across a range of disciplines ... at the intersection of AI and its impact on society.'

Microsoft recognizes the value of bridging academic, industry, policy, and regulatory worlds and seeks to ignite interdisciplinary collaboration that drives real-world impact.

This initial cohort of researchers is divided into subfields. Dr. Jacobs will work on "Sociotechnical Approaches to Measuring Harms Caused by AI Systems". We congratulate Professor Jacobs on this appointment.

While we are discussing accolades, Dr. Jacobs recently co-authored a preprint that is already receiving media coverage: "The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology".  The paper will be published at the ACM (Association of Computing Machinery) CHI conference on Human Factors in Computing Systems in May.  Michigan News writes:

When designers use inaccurate depictions of the human body, the use of artificial intelligence in some applications might not be as safe for those who don’t fit that body type, according to a new study.

These flawed assumptions define what is considered the norm for human bodies, and have made their way into AI through motion capture, said study co-author Abigail Jacobs, assistant professor at the U-M School of Information and Center for the Study of Complex Systems.

The work was also covered by IEEE in the article "AI Is Being Built on Dated, Flawed Motion-Capture Data “:

Diversity of thought in industrial design is crucial: If no one thinks to design a technology for multiple body types, people can get hurt. The invention of seat belts is an oft-cited example of this phenomenon, as they were designed based on crash dummies that had traditionally male proportions, reflecting the bodies of the team members working on them.

The same phenomenon is now at work in the field of motion-capture technology. Throughout history, scientists have endeavored to understand how the human body moves. But how do we define the human body? Decades ago many studies assessed “healthy male” subjects; others used surprising models like dismembered cadavers. Even now, some modern studies used in the design of fall-detection technology rely on methods like hiring stunt actors who pretend to fall.

“Many researchers don’t have access to advanced motion-capture labs to collect data, so we’re increasingly relying on benchmarks and standards to build new tech,” Jacobs says. “But when these benchmarks don’t include representations of all bodies, especially those people who are likely to be involved in real-world use cases—like elderly people who may fall—these standards can be quite flawed.”

She hopes we can learn from past mistakes, such as cameras that didn’t accurately capture all skin tones and seat belts and airbags that didn’t protect people of all shapes and sizes in car crashes.

See more coverage at New research examines how assumptions affect motion capture technology and reprinted the UM article.

Congratulations again!