PhD Defense: Towards Effective and Inclusive AI: Aligning AI Systems with User Needs and Stakeholder Values Across Diverse Contexts

https://umd.zoom.us/my/tristaroom
Abstract:

Inspired by the Turing test, a long line of research in AI has focused on technical improvement on tasks thought to require human-like comprehension. However, this focus has often resulted in models with impressive technical capabilities but uncertain real-world applicability. Despite the advancements of large pre-trained models, we still see various failure cases towards discriminated groups and when applied to specific applications.A major problem here is the detached model development process --- these models are designed, developed, and evaluated with limited consideration of their users and stakeholders. My dissertation is dedicated to addressing this detachment by examining how artificial intelligence (AI) systems can be more effectively aligned with the needs of users and the values of stakeholders across diverse contexts. This work aims to close the gap between the current state of AI technology and its meaningful application in the lives of real-life stakeholders.