Matthew K. Hong

2023

1. Generative AI for Product Design: Getting the Right Design and the Design Right

   Matthew K. Hong, Shabnam Hakimi, Yan-Ying Chen, Heishiro Toyoda, Charlene Wu, and Matthew Klenk

   In arXiv preprint arXiv:2306.01217, 2023

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   Generative AI (GenAI) models excel in their ability to recognize patterns in existing data and generate new and unexpected content. Recent advances have motivated applications of GenAI tools (e.g., Stable Diffusion, ChatGPT) to professional practice across industries, including product design. While these generative capabilities may seem enticing on the surface, certain barriers limit their practical application for real-world use in industry settings. In this position paper, we articulate and situate these barriers within two phases of the product design process, namely "getting the right design" and "getting the design right," and propose a research agenda to stimulate discussions around opportunities for realizing the full potential of GenAI tools in product design.

   @inproceedings{hong2023generative,
     title = {Generative AI for Product Design: Getting the Right Design and the Design Right},
     author = {Hong, Matthew K. and Hakimi, Shabnam and Chen, Yan-Ying and Toyoda, Heishiro and Wu, Charlene and Klenk, Matthew},
     booktitle = {arXiv preprint arXiv:2306.01217},
     pages = {1--3},
     year = {2023}
   }

2. 

   Next Steps for Human-Centered Generative AI: A Technical Perspective

   Xiang ’Anthony’ Chen, Jeff Burke, Ruofei Du, Matthew K. Hong, Jennifer Jacobs, Philippe Laban, Dingzeyu Li, Nanyun Peng, Karl D. D. Willis, Chien-Sheng Wu, and  others

   arXiv preprint arXiv:2306.15774, 2023

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   Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI) from a technical perspective. We contribute a roadmap that lays out future directions of Generative AI spanning three levels: Aligning with human values; Accommodating humans’ expression of intents; and Augmenting humans’ abilities in a collaborative workflow. This roadmap intends to draw interdisciplinary research teams to a comprehensive list of emergent ideas in HGAI, identifying their interested topics while maintaining a coherent big picture of the future work landscape.

   @article{chen2023next,
     title = {Next Steps for Human-Centered Generative AI: A Technical Perspective},
     author = {Chen, Xiang 'Anthony' and Burke, Jeff and Du, Ruofei and Hong, Matthew K. and Jacobs, Jennifer and Laban, Philippe and Li, Dingzeyu and Peng, Nanyun and Willis, Karl D. D. and Wu, Chien-Sheng and others},
     journal = {arXiv preprint arXiv:2306.15774},
     pages = {1--34},
     year = {2023}
   }

2021

1. 

   Planning for Natural Language Failures with the AI Playbook

   Matthew K Hong, Adam Fourney, Derek DeBellis, and Saleema Amershi

   In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021

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   Prototyping AI user experiences is challenging due in part to probabilistic AI models making it difficult to anticipate, test, and mitigate AI failures before deployment. In this work, we set out to support practitioners with early AI prototyping, with a focus on natural language (NL)-based technologies. Our interviews with 12 NL practitioners from a large technology company revealed that, in addition to challenges prototyping AI, prototyping was often not happening at all or focused only on idealized scenarios due to a lack of tools and tight timelines. These findings informed our design of the AI Playbook, an interactive and low-cost tool we developed to encourage proactive and systematic consideration of AI errors before deployment. Our evaluation of the AI Playbook demonstrates its potential to 1) encourage product teams to prioritize both ideal and failure scenarios, 2) standardize the articulation of AI failures from a user experience perspective, and 3) act as a boundary object between user experience designers, data scientists, and engineers.

   @inproceedings{hong2021planning,
     title = {Planning for Natural Language Failures with the AI Playbook},
     author = {Hong, Matthew K and Fourney, Adam and DeBellis, Derek and Amershi, Saleema},
     booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
     year = {2021}
   }