Matt Artz | Source | Founder at Azimuth Labs

We asked folks across various industries and roles how they envision AI changing the way they work and the way their business is run. Here's what they said.

Matt Artz is a business anthropologist exploring the effects of technology on society. As the Head of Product and Experience at Cloudshadow and the Founder and Principal Researcher of Azimuth Labs, Matt works at the intersection of research, design, and strategy. In these roles, he advocates for the responsible development of technology. His anthropology research focuses on the benefits and risks of consumer DNA testing. Outside of work, you can find Matt writing, speaking, composing music and photographing the everyday life of people around the world. Mr. Artz is an applied anthropologist working as a User Experience (UX) researcher at the intersection of technology and society. On a daily basis, he oversees the product development process of software, but in doing that work, he infuses social theory to ensure building products that are not only usable, but desirable and just. Most of his private consulting is focused in the health...

Algorithms used by colleges and universities to determine financial aid for prospective students may be unfairly filtering out low-income, female, and non-white applicants who are more likely to need larger aid packages. The widespread use of enrollment management algorithms to boost overall enrollment and improve fiscal planning could prove counterproductive to diversity goals without proper oversight, according to a recent report from the Brookings Institution. The report recommended that schools hire personnel to evaluate the quality of algorithms, while state policymakers should create more transparency about their use in public institutions. Matt Artz, a design anthropologist and instructor at Drew University in New Jersey, warns that algorithms used in higher education can work similarly to those used by platforms such as Spotify, favoring people with more social and financial capital. He notes that algorithms often mirror human prejudices and designing helpful algorithms must involve substantial input from stakeholders, including students, universities, ed-tech developers, and others, to mitigate biases before they produce real-world consequences. Artz believes that involving stakeholders in crafting the research agenda and using a participatory model will produce outcomes that are naturally more equitable.