Sasha Mitts joins Stephanie Vance and Molly Strawn-Carreño to explore how AI is changing research and why researchers need to be careful about what they hand over to machines. As an independent researcher, Sasha has seen firsthand how AI can accelerate analysis while quietly introducing errors, confirmation bias, and generic conclusions.

The conversation examines practical ways to build more rigorous AI-assisted research workflows. Sasha explains why researchers should document their assumptions before using AI, validate AI-generated coding across multiple models, and break complex analysis into discrete, repeatable steps. Rather than asking AI to interpret an entire dataset and deliver a conclusion, researchers can use it for tasks such as data cleaning, pattern recognition, coding, and statistical analysis while retaining responsibility for the bigger picture.

The episode also explores what AI cannot replace: storytelling, contextual awareness, emotional intelligence, and relationships with stakeholders. Ultimately, Sasha argues that the future of research belongs to those who use AI to amplify their perspective, not replace it.

About the Guest:
Sasha Mitts is an independent researcher, formerly a UX researcher at Meta, focused on the intersection of AI and human-centered research.  His work explores how to evaluate AI based on how people actually experience it, uncover where technology falls short, and turn powerful capabilities into meaningful value. He approaches AI as a stewardship challenge, emphasizing thoughtful design, critical questioning, and long-term human benefit.