AINeedsPlanner: A Workbook to Support Effective Collaboration Between AI Experts and Clients
arXiv:2402.08938 · doi:10.1145/3643834.3661577
Abstract
Clients often partner with AI experts to develop AI applications tailored to their needs. In these partnerships, careful planning and clear communication are critical, as inaccurate or incomplete specifications can result in misaligned model characteristics, expensive reworks, and potential friction between collaborators. Unfortunately, given the complexity of requirements ranging from functionality, data, and governance, effective guidelines for collaborative specification of requirements in client-AI expert collaborations are missing. In this work, we introduce AINeedsPlanner, a workbook that AI experts and clients can use to facilitate effective interchange and clear specifications. The workbook is based on (1) an interview of 10 completed AI application project teams, which identifies and characterizes steps in AI application planning and (2) a study with 12 AI experts, which defines a taxonomy of AI experts' information needs and dimensions that affect the information needs. Finally, we demonstrate the workbook's utility with two case studies in real-world settings.
arXiv commentTo appear in DIS 2024
References in corpus (10)
- Model Cards for Model Reporting
- AutoML: A Survey of the State-of-the-Art
- Improving fairness in machine learning systems: What do industry practitioners need?
- Questioning the AI: Informing Design Practices for Explainable AI User Experiences
- Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
- Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions
- How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
- How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?
- Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook
- Symphony: Composing Interactive Interfaces for Machine Learning