CIMPLE: Countering Creative Information Manipulation with Explainable AI

可解释性对于迈向可信、负责任和道德的人工智能至关重要，但仍处于起步阶段。大多数相关工作都集中在提高人工智能模型设计和训练数据的透明度，以及对最终决策的基于知识的解释上。这种解释的可理解性及其对特定用户和应用领域的适用性迄今为止很少受到关注。因此，需要在XAI方法中进行跨学科和激烈的演变，以设计更易于理解，可重新配置和个性化的解释。知识图为更好地构建AI模型的核心提供了巨大的潜力，并在为决策提供解释时使用语义表示。通过以粒度的方式捕获上下文和应用领域，这些图提供了一个非常需要的语义层，这是目前典型的蛮力机器学习方法所缺少的。人为因素是相关AI模型成功的关键决定因素。在某些情况下，例如错误信息检测，现有的XAI技术可解释性方法不足以作为领域的复杂性和各种相关的社会和心理因素可以严重影响用户对派生解释的信任。过去的研究表明，向用户提供真/假可信度决策是不充分和无效的，特别是当使用黑盒算法时。为此，CIMPLE的目标是尝试创新的社会和知识驱动的人工智能解释，并使用计算创造力技术为相当复杂的人工智能决策和行为生成强大，引人入胜，易于理解的解释。这些解释将在检测和跟踪被操纵信息的领域进行测试，同时考虑到社会、心理和技术上的可解释性需求和要求。

Explainability is of significant importance in the move towards trusted, responsible and ethical AI, yet remains in infancy. Most relevant efforts focus on the increased transparency of AI model design and training data, and on statistics-based interpretations of resulting decisions. The understandability of such explanations and their suitability to particular users and application domains received very little attention so far. Hence there is a need for an interdisciplinary and drastic evolution in XAI methods, to design more understandable, reconfigurable and personalisable explanations. Knowledge Graphs offer significant potential to better structure the core of AI models, and to use semantic representations when producing explanations for their decisions. By capturing the context and application domain in a granular manner, such graphs offer a much needed semantic layer that is currently missing from typical brute-force machine learning approaches.Human factors are key determinants of the success of relevant AI models. In some contexts, such as misinformation detection, existing XAI technical explainability methods do not suffice as the complexity of the domain and the variety of relevant social and psychological factors can heavily influence users' trust in derived explanations. Past research has shown that presenting users with true / false credibility decisions is inadequate and ineffective, particularly when a black-box algorithm is used. To this end, CIMPLE aims to experiment with innovative social and knowledge-driven AI explanations, and to use computational creativity techniques to generate powerful, engaging, and easily and quickly understandable explanations of rather complex AI decisions and behaviour. These explanations will be tested in the domain of detection and tracking of manipulated information, taking into account social, psychological and technical explainability needs and requirements.