Stanford Researchers Champion Cautious, Rigorous AI Development to Ensure Safe, Sustainable Innovation

Stanford researchers are redefining the pace and purpose of AI innovation by prioritizing caution, depth, and long-term impact over rapid deployment. While Silicon Valley’s “move fast and break things” ethos dominates tech culture, Stanford’s approach emphasizes rigorous scrutiny, interdisciplinary collaboration, and a commitment to understanding the fundamental mechanisms behind AI systems. Yuyan Wang, an assistant professor of marketing at the Graduate School of Business, left industry roles at Uber and Google DeepMind to pursue foundational research. She was frustrated by the lack of transparency in AI models that shape user experiences, such as YouTube recommendations. “The models have no understanding of why people make choices,” she said. Her work now focuses on building AI systems grounded in behavioral theory and economics, aiming for transparency and long-term reliability rather than short-term gains. In the field of sustainability, Jef Caers, professor of Earth and planetary sciences, leads Mineral X, an initiative using AI to identify and responsibly source critical minerals like copper and lithium for the energy transition. In a landmark 2024 discovery, AI developed in Caers’ lab helped pinpoint a high-grade copper deposit with minimal drilling, reducing uncertainty and environmental impact. His team combines vast, multi-layered datasets—including historical maps and subsurface geophysical measurements—with AI models inspired by those used in self-driving cars and chess. He collaborates with Mykel Kochenderfer to ensure safety and robustness in high-uncertainty environments. Aditi Sheshadri, assistant professor of Earth system science, uses AI to study atmospheric gravity waves—tiny but powerful forces that influence climate patterns. These waves are too small to be captured by current climate models, creating major uncertainties. Her Datawave project unites global observations, simulations, and AI to improve climate predictions. “We need to be careful about how we interpret data,” she notes, especially when using current climate data to predict future conditions. In law, Liftlab, led by Megan Ma and Julian Nyarko at Stanford Law School, aims to separate AI hype from real impact. The lab evaluates tools that improve legal education and practice, from AI-powered contract drafting to bias detection systems. “Legal AI should serve human judgment, not replace it,” Ma emphasizes. The goal is to enhance advocacy, accountability, and client-centered care. In health care, Roxana Daneshjou, assistant professor of biomedical data science and dermatology, develops AI tools with a strong focus on safety and equity. Her research includes chatbots for patient record navigation and multi-modal systems that analyze both text and images to predict outcomes. She warns against deploying untested AI in clinical settings. “You can’t move fast and break things when lives are at stake,” she says. Her team even tested LLMs with 80 experts, uncovering troubling tendencies like sycophantic behavior—where models tell users what they want to hear. Dora Demszky, assistant professor of education data science, focuses on AI that supports teachers, not students directly. Her lab builds tools that analyze classroom conversations, adapt curricula, and create inclusive learning materials. “Teachers need oversight,” she says. Projects are designed with diverse teaching experience and student needs in mind, including language learners and those below grade level. Chelsea Finn, assistant professor of computer science and electrical engineering, pioneers AI for robotics. Her lab created Mobile ALOHA, a robot capable of cooking shrimp, but acknowledges the challenge of generalization—performing tasks in any environment. To overcome this, she launched DROID, a large open-source dataset of robot interactions from 50 buildings across 15 institutions. Her work on “vision, language, action” models enables robots to respond to natural language and visual cues. Laura Gwilliams, assistant professor of psychology and neuroscience, uses LLMs to study how the human brain processes language. By “lesioning” models to mimic stroke effects, her team explores whether AI can simulate aphasia. “We’re probing an alien system to see if it mirrors human cognition,” she says. AI allows faster, more natural experiments, but requires careful interpretation. Brian Trippe, assistant professor of statistics, applies machine learning to protein structure prediction. Building on Nobel-winning AI, his work aims to design precise, safe therapeutics by modeling proteins’ dynamic shapes. “Data is key to understanding cells and designing treatments,” he says. Together, these researchers exemplify a thoughtful, responsible path forward—where AI advances not just in capability, but in trust, transparency, and human-centered design.