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On Demand

Qcast - So You’ve Added an AI Assistant to Your Research Team: Understanding Biases of LLMs


Total Credits: 1 Advance Credits

Average Rating:
   3
Categories:
Analysis & Reporting |  Business Practices |  Professionalism |  Research Design
Speaker:
Alexandra Jayeun Lee
Duration:
1 hour
Format:
Audio and Video
Original Program Date:
Jul 19, 2024



Overview

JULY 2024 QCAST

The recent initial surge in popularity of Open AI's ChatGPT and consequential growth of Large Language Models (LLMs) and the recently released multi-modal GPT-4o has brought the concept of prompt engineering into the spotlight, raising important questions about its potential to augment many different parts of our jobs, including some of the common applications in user research such as summarization and synthesis. 
 
In this QCast, we will delve into the world of conversational AI assistants and explore the potential biases, limitations, and characteristics of typical AI tools available in the mainstream today to offer some insights for qualitative researchers seeking to improve their interactions with these tools, and discuss the challenges and opportunities presented by these AI assistants.  
 
In this session, we’ll learn how to navigate potential pitfalls, enhance our prompt engineering skills, and discover creative ways to adapt digital assistants to existing processes. Attendees will gain a deeper understanding of the current capabilities and limitations of AI assistants, setting realistic expectations and exploring ethical considerations for the future.  

 

 

Resources

Speaker

Alexandra Jayeun Lee's Profile

Alexandra Jayeun Lee Related Seminars and Products

Director of Research and Data Science

Microsoft


Alexandra Lee is the Principal Director of Research and Data Science for Microsoft Copilot Studios. With passion for human centered design and co-creating data-driven solutions, Alex continues to evolve her foundational training in architecture and service design, to serve and empower teams to achieve more with computational design thinking.