Ethical affective computing (AC) requires maximizing the benefits to users while minimizing its harm to obtain trust from users. This requires responsible development and deployment to ensure fairness, bias mitigation, privacy preservation, and accountability. To obtain this, we require methodologies that can quantify, visualize, analyze, and mine insights from affect data. Hence, in this article, we propose a spatio-temporal model for representing secondary affect data from network sciences’ perspective. We propose a network science-based model to represent spatio-temporal data, e.g., action units’ sequences, and continuous affect reports. In particular, the proposed model captures the spatial and temporal strength of the relationship among essential variables in the data. The proposed model allows to analyze data as a whole system. We also demonstrated the use case of the model for graph analytics on secondary affect data that can assist to measure and quantify several issues that can be originated from the study setup, data recording devices, and the influences/biases that can originate from the perspective of the affect reporters. We also demonstrated the use cases of the proposed method on ethical trustworthy emotional AI via measuring biases from de-identified data and how it contributes towards ethics, transparency, value alignment, and governance.

中文翻译：

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次要情感数据的时空图分析，用于改进值得信赖的情感人工智能

道德情感计算（AC）要求最大限度地为用户带来利益，同时将其危害最小化，以获得用户的信任。这需要负责任的开发和部署，以确保公平、减少偏见、保护隐私和问责制。为了实现这一点，我们需要能够量化、可视化、分析和挖掘影响数据洞察的方法。因此，在本文中，我们提出了一种时空模型，用于从网络科学的角度表示次级情感数据。我们提出了一种基于网络科学的模型来表示时空数据，例如动作单元的序列和连续的情感报告。特别是，所提出的模型捕获了数据中基本变量之间关系的空间和时间强度。所提出的模型允许将数据作为整个系统进行分析。我们还演示了对次要影响数据进行图形分析的模型的用例，该模型可以帮助测量和量化可能源自研究设置、数据记录设备以及可能源自以下角度的影响/偏差的几个问题：影响记者。我们还通过测量去识别数据的偏差以及它如何对道德、透明度、价值一致性和治理做出贡献，展示了所提出的道德可信情感人工智能方法的用例。