Michigan Tech Students Discuss AI, Ethics, Learning and Careers

Summary

Michigan Tech data science majors Alisa Teige, Diana Shadibaeva, and Tyson Watson describe why they founded the ML/AI Club during the 2025–26 academic year: students needed a low-pressure community for collaboration, experimentation, and machine-learning competitions. In the Q&A, all three argue that AI should generally assist people rather than become the final decision-maker in areas such as law, medicine, economics, or social issues, although they differ on where replacement should be prohibited. They also disagree about explainability in high-stakes settings: Watson would favor a 99% accurate opaque system, while Shadibaeva and Teige prefer a less accurate system whose reasoning can be checked, with human judgment retained. The students are skeptical that generative AI will develop human-like understanding because it learns mathematical patterns, but Watson says sufficiently useful behavior may make the distinction less important. On careers, they view AI specialization and software engineering as related rather than mutually exclusive paths; Teige adds that everyone may need some ability to use AI effectively. They warn that people often overtrust AI, let it do too much of their writing or coding, and fail to verify outputs, while recommending it for filling knowledge gaps and assisting work. The discussion also notes the historical influence of ELIZA, an early chatbot that some users believed understood them. A related student explainer distinguishes AI as the broad goal from machine learning as a data-driven subset, tracing applications from spam filtering and recommendations to medical diagnosis, fraud detection, and autonomous systems.