# MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing

arXiv: https://arxiv.org/abs/2409.07256 · HTML: https://mlsanity.com/paper/2409.07256/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition](https://mlsanity.com/paper/2407.03835/) (2024)
- [Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization](https://mlsanity.com/paper/2305.01979/) (2023)
- [K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations](https://mlsanity.com/paper/2005.04120/) (2020)
- [A Wide Evaluation of ChatGPT on Affective Computing Tasks](https://mlsanity.com/paper/2308.13911/) (2023)
- [GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing](https://mlsanity.com/paper/2403.05916/) (2024)
- [JRDB-Social: A Multifaceted Robotic Dataset for Understanding of Context and Dynamics of Human Interactions Within Social Groups](https://mlsanity.com/paper/2404.04458/) (2024)
- [Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond](https://mlsanity.com/paper/2402.13589/) (2024)
- [Predicting Group Cohesiveness in Images](https://mlsanity.com/paper/1812.11771/) (2018)
- [Emolysis: A Multimodal Open-Source Group Emotion Analysis and Visualization Toolkit](https://mlsanity.com/paper/2305.05255/) (2023)
- [Disguising Personal Identity Information in EEG Signals](https://mlsanity.com/paper/2010.08915/) (2020)
- [Privacy Constrained Fairness Estimation for Decision Trees](https://mlsanity.com/paper/2312.08413/) (2023)

## Built on by (most cited first, 1 of 1)

- [SocialGesture: Delving into Multi-person Gesture Understanding](https://mlsanity.com/paper/2504.02244/) (2025)

## Abstract (arXiv)

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/