The University of Texas has designated 2024 as its “Year of AI,” a strategic initiative aimed at advancing artificial intelligence research and cultivating the next generation of experts in the field. While the program signals a commitment to technological leadership, faculty members emphasize that innovation must be accompanied by rigorous attention to the ethical dimensions of AI development.
Computer science professor Swarat Chaudhuri underscores the inevitability of bias in AI systems. “Even carefully collected data will often reflect problems or inequities in a society,” Chaudhuri said. He explains that an AI algorithm learns patterns embedded in data and then acts upon them, meaning any societal imbalance present in the data can be replicated in the system’s outputs.
Matthew Lease, a professor in the School of Information, expands on this point, noting the difficulty of disentangling bias from real-world datasets. “The challenge, given that we collect data from the world and the world is a biased place, is how to evaluate how our models are reproducing that bias that exists in the world,” Lease said. He points out that the mechanics of algorithmic training themselves can exacerbate inequities. “The way algorithms work is the more data you have to train the AI, the better they do. And so, just simply by lack of data, a group that is underrepresented in data will tend to have lower performance,” Lease said.
Chaudhuri cites a study on predictive policing in Oakland, California, as a clear example of bias amplification. Police used an algorithm to forecast areas with high likelihood of drug crimes based on historical arrest records. Because those records reflected prior over-policing in low-income, minority neighborhoods, the algorithm disproportionately flagged those areas. This created what Chaudhuri calls a “feedback loop,” where biased inputs lead to biased outputs, which then reinforce the original skew. “Applied naively, this algorithm will then send more cops to those areas, and then naturally, when you send police officers to an area, they also have the tendency to see more (crime),” Chaudhuri said.
The phenomenon is not confined to law enforcement. Samantha Shorey, assistant professor of communication studies, points to AI-driven hiring systems as another domain where automation can perpetuate discrimination. “There’s already human bias built into hiring,” Shorey said. “When we seek to automate that process, our first thought is, maybe that’s a way of overcoming bias, when, in actuality, oftentimes what ends up happening is we build those biases into the system.”
Addressing these challenges requires deliberate engagement with diverse communities and stakeholders. Shorey stresses the importance of inclusive design teams. “Having greater representation of the people that design and produce AI technologies can help create technologies that are better able to render the diversity of human experience,” she said.
One initiative embodying this approach is Good Systems, a university research project focused on the ethical implications of AI. By sponsoring programs like Good Systems, UT seeks to integrate inclusivity into the fabric of AI development. Lease articulates the guiding philosophy: “The big idea we have is that if we want to make ethical and responsible AI, you can’t do it in a vacuum. You have to think about a societal challenge you want to help solve.”
For engineers, technologists, and students immersed in fields from aerospace to robotics, these discussions resonate beyond academia. The same principles apply to autonomous drones navigating complex environments, machine vision systems in manufacturing, or predictive maintenance algorithms in automotive engineering. In each case, data quality, representativeness, and ethical oversight directly influence system performance and societal impact.
UT’s “Year of AI” thus serves as both a technological milestone and a reminder: progress in artificial intelligence must be matched by a commitment to understanding and mitigating bias. As research expands, the university’s diverse student body and collaborative ethos offer a foundation for developing AI systems that reflect and respect the full spectrum of human experience.