Sydney Lee

2papers

2 Papers

5.6HCJun 23
Human-Centered Design: The Disclosure of Generative Artificial Intelligence for Emerging Professionals

Sydney Lee

As the Human centered design continues to grow, generative AI has the potential to streamline the research process by iterating tasks within established workflows to increase efficiency. However, integrating AI raises concerns surrounding ethical bias, complexity, and the lack of prioritization of humanistic values. Emerging professionals represent a cohort with the opportunity to learn Human Centered Design principles, yet without this foundation AI becomes more of a crutch than a tool, leading to reduced experience with deep work, decreased autonomy, and deskilling of key foundations. Disclosures are a common method to self report AI usage, but they provide little clarification on appropriate implementation and may encourage omission to avoid consequences. This paper reflects on experiences in the Human Centered Design course ITIS8300, which emphasized optimizing user experience, enhancing innovation and collaboration, and improving efficiency through iterative user feedback. A semester long project, structured through milestones and team roles including a generative AI advocate, resulted in a high level disclosure report detailing design processes, methodology, findings, and rationale for AI usage. The course offered freedom in execution while setting clear boundaries for incorporating human feedback, reinforcing justification for HCI workflows and encouraging transparent AI use. This approach mirrors an industry with minimal regulation, demonstrating that when AI usage is safe, justified, and transparent, it can significantly advance the field through AI augmented workflows and support co creation an increase productivity.

31.0CLOct 27, 2020Code
WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols

Jeniya Tabassum, Sydney Lee, Wei Xu et al.

This paper presents the results of the wet lab information extraction task at WNUT 2020. This task consisted of two sub tasks: (1) a Named Entity Recognition (NER) task with 13 participants and (2) a Relation Extraction (RE) task with 2 participants. We outline the task, data annotation process, corpus statistics, and provide a high-level overview of the participating systems for each sub task.