Matthew K. Hong

2023

1. 

   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}
   }

2. 

   DesignAID: Using Generative AI and Semantic Diversity for Design Inspiration

   Alice Cai, Steven R Rick, Jennifer L Heyman, Yanxia Zhang, Alexandre Filipowicz, Matthew Hong, Matt Klenk, and Thomas Malone

   In Proceedings of The ACM Collective Intelligence Conference, 2023

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   Designers often struggle to sufficiently explore large design spaces, which can lead to design fixation and suboptimal outcomes. Here we introduce DesignAID, a generative AI tool that supports broader design space exploration by first using large language models to produce a range of diverse ideas expressed in words, and then using image generation software to create images from these words. This innovative combination of AI-based capabilities allows human-computer pairs to rapidly create a diverse set of visual concepts without time-consuming drawing. In a study with 87 crowd-sourced designers, we found that designers rated the automatic generation of images from words as significantly more inspirational, enjoyable, and useful than a conventional baseline condition of image search using Pinterest. Surprisingly, however, we found that automatically generating highly diverse ideas had less value. For image generation, the high diversity condition was somewhat better in inspiration but no better in the other dimensions, and for image search it was significantly worse in all dimensions.

   @inproceedings{cai2023designaid,
     title = {DesignAID: Using Generative AI and Semantic Diversity for Design Inspiration},
     author = {Cai, Alice and Rick, Steven R and Heyman, Jennifer L and Zhang, Yanxia and Filipowicz, Alexandre and Hong, Matthew and Klenk, Matt and Malone, Thomas},
     booktitle = {Proceedings of The ACM Collective Intelligence Conference},
     pages = {1--11},
     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}
   }