MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing
<|reference_start|>MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing: With the rapid advancements in multimodal generative technology, Affective Computing research has provoked discussion about the potential consequences of AI systems equipped with emotional intelligence. Affective Computing involves the design, evaluation, and implementation of Emotion AI and related technologies aimed at improving people's lives. Designing a computational model in affective computing requires vast amounts of multimodal data, including RGB images, video, audio, text, and physiological signals. Moreover, Affective Computing research is deeply engaged with ethical considerations at various stages-from training emotionally intelligent models on large-scale human data to deploying these models in specific applications. Fundamentally, the development of any AI system must prioritize its impact on humans, aiming to augment and enhance human abilities rather than replace them, while drawing inspiration from human intelligence in a safe and responsible manner. The MRAC 2024 Track 1 workshop seeks to extend these principles from controlled, small-scale lab environments to real-world, large-scale contexts, emphasizing responsible development. The workshop also aims to highlight the potential implications of generative technology, along with the ethical consequences of its use, to researchers and industry professionals. To the best of our knowledge, this is the first workshop series to comprehensively address the full spectrum of multimodal, generative affective computing from a responsible AI perspective, and this is the second iteration of this workshop. Webpage: https://react-ws.github.io/2024/<|reference_end|>
@article{ghosh2024mrac,
    title={MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible
  Affective Computing},
    author={Shreya Ghosh, Zhixi Cai, Abhinav Dhall, Dimitrios Kollias, Roland
  Goecke, Tom Gedeon},
    journal={arXiv preprint arXiv:2409.07256},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07256},
    primaryClass={cs.CV}
}