CIMPLE: Countering Creative Information Manipulation with Explainable AI

在迈向可信、负责任和道德的人工智能的过程中，可解释性具有重要意义，但仍处于初级阶段。最相关的努力侧重于提高人工智能模型设计和培训数据的透明度，以及对所产生的决定进行基于统计的解释。到目前为止，这种解释的可理解性及其对特定用户和应用程序领域的适用性很少受到关注。因此，有必要在XAI方法中进行跨学科和剧烈的演变，以设计更易理解、可重构和个性化的解释。知识图为更好地构建人工智能模型的核心，并在为其决策产生解释时使用语义表示提供了巨大的潜力。通过以细粒度的方式捕获上下文和应用领域，这种图提供了目前典型的暴力机器学习方法所缺少的急需的语义层。人的因素是相关人工智能模型成功的关键决定因素。在某些情况下，如错误信息检测，现有的XAI技术可解释性方法是不够的，因为领域的复杂性和相关社会和心理因素的多样性会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定是不充分和无效的，特别是在使用黑盒算法时。为此，CIMPLE旨在试验创新的社交和知识驱动的人工智能解释，并使用计算创造力技术对相当复杂的人工智能决策和行为产生强大、吸引人的、容易和快速理解的解释。这些解释将在检测和跟踪被操纵的信息领域进行测试，同时考虑到社会、心理和技术可解释性的需要和要求。

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.