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

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arxiv 2306.15774 v2 pith:TZFVQCDH submitted 2023-06-27 cs.HC cs.CLcs.CVcs.LG

Xiang 'Anthony' Chen , Jeff Burke , Ruofei Du , Matthew K. Hong , Jennifer Jacobs , Philippe Laban , Dingzeyu Li , Nanyun Peng
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Karl D. D. Willis Chien-Sheng Wu Bolei Zhou
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classification cs.HCcs.CLcs.CVcs.LG
keywords generativehumancoherentfuturehgaihuman-centerednext-stepsresearch
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Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI). We contribute a comprehensive research agenda that lays out future directions of Generative AI spanning three levels: aligning with human values; assimilating human intents; and augmenting human abilities. By identifying these next-steps, we intend to draw interdisciplinary research teams to pursue a coherent set of emergent ideas in HGAI, focusing on their interested topics while maintaining a coherent big picture of the future work landscape.

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Cited by 2 Pith papers

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  1. GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design

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    GenTune improves AI image refinement by tracing image regions back to prompt labels and allowing element-level, semantic-guided edits.

  2. Seamless and Efficient Interactions within a Mixed-Dimensional Information Space

    cs.HC 2025-06 conditional novelty 4.0 of 10

    A thesis that three design strategies, multimodal AI, context-aware placement, and combined 2D/3D views, make mixed-dimensional information spaces seamless and efficient, demonstrated with three systems.

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