小而公平！面向多模态人人与机器人-人类心理健康辅导的公平性
Small but Fair! Fairness for Multimodal Human-Human and Robot-Human Mental Wellbeing Coaching
AI总结：
针对HRI中机器学习偏见及小型多模态数据集去偏难题，本文分析人人与机器人-人类健康辅导数据，提出MixFeat数据增强策略并给出公平性研究建议。
摘要：
近年来，情感计算（AC）与人机交互（HRI）研究界已将公平性置于其研究议程的核心。然而，现有工作均未解决HRI场景中的机器学习（ML）偏见问题。此外，当前许多用于AC和HRI的数据集规模“较小”，使ML偏见与去偏分析颇具挑战。本文提出首个探索ML偏见分析与缓解的工作，研究对象是在人人及机器人-人类健康辅导两种场景中收集的三个小型多模态数据集。本工作贡献包括：i）首次探索HRI场景中的ML偏见与公平性问题；ii）提供多模态分析，通过建模性能和公平性指标在高层与低层特征上进行评估，并提出一种简单有效的数据增强策略（MixFeat），对本文给出的小型数据集去偏；iii）开展大量实验与分析，揭示AC和HRI研究特有的ML公平性洞见，从而提炼出一组建议，帮助AC和HRI研究者更多参与公平感知的、基于ML的研究。
英文摘要：
In recent years, the affective computing (AC) and human-robot interaction (HRI) research communities have put fairness at the centre of their research agenda. However, none of the existing work has addressed the problem of machine learning (ML) bias in HRI settings. In addition, many of the current datasets for AC and HRI are "small", making ML bias and debias analysis challenging. This paper presents the first work to explore ML bias analysis and mitigation of three small multimodal datasets collected within both a human-human and robot-human wellbeing coaching settings. The contributions of this work includes: i) being the first to explore the problem of ML bias and fairness within HRI settings; and ii) providing a multimodal analysis evaluated via modelling performance and fairness metrics across both high and low-level features and proposing a simple and effective data augmentation strategy (MixFeat) to debias the small datasets presented within this paper; and iii) conducting extensive experimentation and analyses to reveal ML fairness insights unique to AC and HRI research in order to distill a set of recommendations to aid AC and HRI researchers to be more engaged with fairness-aware ML-based research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
