Graph Neural Networks (GNNs) are widely adopted in advanced AI systems due to their capability of representation learning on graph data. Even though GNN explanation is crucial to increase user trust in the systems, it is challenging due to the complexity of GNN execution. Lately, many works have been proposed to address some of the issues in GNN explanation. However, they lack generalization capability or suffer from computational burden when the size of graphs is enormous. To address these challenges, we propose a multi-level GNN explanation framework based on an observation that GNN is a multimodal learning process of multiple components in graph data. The complexity of the original problem is relaxed by breaking into multiple sub-parts represented as a hierarchical structure. The top-level explanation aims at specifying the contribution of each component to the model execution and predictions, while fine-grained levels focus on feature attribution and graph structure attribution analysis based on knowledge distillation. Student models are trained in standalone modes and are responsible for capturing different teacher behaviors, later used for particular component interpretation. Besides, we also aim for personalized explanations as the framework can generate different results based on user preferences. Finally, extensive experiments demonstrate the effectiveness and fidelity of our proposed approach.

This article was published as a part of the Data Science Blogathon. In the modern day, where there is a colossal amount of data at our disposal, using ML models to make decisions has become crucial in sectors like healthcare, finance, marketing, etc. Many ML models are black boxes since it is difficult to fully understand how they function after training. This makes it difficult to understand and explain a model's behaviour, but it is important to do so to have trust in its accuracy. So how can we build trust in the predictions of a black box?

We present an enrichment of the Hateval corpus of hate speech tweets (Basile et. al 2019) aimed to facilitate automated counter-narrative generation. Comparably to previous work (Chung et. al. 2019), manually written counter-narratives are associated to tweets. However, this information alone seems insufficient to obtain satisfactory language models for counter-narrative generation. That is why we have also annotated tweets with argumentative information based on Wagemanns (2016), that we believe can help in building convincing and effective counter-narratives for hate speech against particular groups. We discuss adequacies and difficulties of this annotation process and present several baselines for automatic detection of the annotated elements. Preliminary results show that automatic annotators perform close to human annotators to detect some aspects of argumentation, while others only reach low or moderate level of inter-annotator agreement.

Recent work shows issues of consistency with explanations, with methods generating local explanations that seem reasonable instance-wise, but that are inconsistent across instances. This suggests not only that instance-wise explanations can be unreliable, but mainly that, when interacting with a system via multiple inputs, a user may actually lose confidence in the system. To better analyse this issue, in this work we treat explanations as objects that can be subject to reasoning and present a formal model of the interactive scenario between user and system, via sequences of inputs, outputs, and explanations. We argue that explanations can be thought of as committing to some model behaviour (even if only prima facie), suggesting a form of entailment, which, we argue, should be thought of as non-monotonic. This allows: 1) to solve some considered inconsistencies in explanation, such as via a specificity relation; 2) to consider properties from the non-monotonic reasoning literature and discuss their desirability, gaining more insight on the interactive explanation scenario.

Type 2 diabetes mellitus is a metabolic disorder of glucose management, whose prevalence is increasing inexorably worldwide. Adherence to therapies, along with a healthy lifestyle can help prevent the onset of disease. This preliminary study proposes the use of explainable artificial intelligence techniques with the aim of (i) characterizing diabetic patients through a set of easily interpretable rules and (ii) providing individualized recommendations for the prevention of the onset of the disease through the generation of counterfactual explanations, based on minimal variations of biomarkers routinely collected in primary care. The results of this preliminary study parallel findings from the literature as differences in biomarkers between patients with and without diabetes are observed for fasting blood sugar, body mass index, and high-density lipoprotein levels.

TL;DR: OmniXAI (short for Omni eXplainable AI) is designed to address many of the pain points in explaining decisions made by AI models. This open-source library aims to provide data scientists, machine learning engineers, and researchers with a one-stop Explainable AI (XAI) solution to analyze, debug, and interpret their AI models for various data types in a wide range of tasks and applications. OmniXAI's powerful features and integrated framework make it a major addition to the burgeoning field of XAI. With the rapidly growing adoption of AI models in real-world applications, AI decision making can potentially have a huge societal impact, especially for application domains such as healthcare, education, and finance. However, many AI models, especially those based on deep neural networks, effectively work as black-box models that lack explainability.

Explainability has taken on more urgency at many banks as a result of increasingly complex AI algorithms, many of which have become critical to the deployment of advanced AI applications in banking, such as facial or voice recognition, securities trading, and cybersecurity. The complexity is due to greater computing power, the explosion of big data, and advances in modeling techniques such as neural networks and deep learning. Several banks are establishing special task forces to spearhead explainability initiatives in coordination with their AI teams and business units. They are also stepping up their oversight of vendor solutions as the use of automated machine learning capabilities continues to grow considerably. Explainability is also becoming a more pressing concern for banking regulators who want to be assured that AI processes and outcomes can be reasonably understood by bank employees.

Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way. Otherwise, they will be unlikely to trust the predictive monitoring technology and, hence, adopt it. This paper proposes a predictive analytics framework that is also equipped with explanation capabilities based on the game theory of Shapley Values. The framework has been implemented in the IBM Process Mining suite and commercialized for business users. The framework has been tested on real-life event data to assess the quality of the predictions and the corresponding evaluations. In particular, a user evaluation has been performed in order to understand if the explanations provided by the system were intelligible to process stakeholders.

Tech Mahindra and Mahindra University have signed a memorandum of understanding (MoU) to set up a new'Makers Lab' for research and development in quantum computing, explainable artificial intelligence, and Metaverse. Tech Mahindra already has 10 Makers Lab across the world and the new unit at Mahindra University will be the 11th facility globally and second in Hyderabad. Emphasising the need to focus on development of quantum computing, Tech Mahindra MD and CEO CP Gurnani said, the industry is looking at data explosion with growth in cloud computing, data centres, and 5G driving the change in the present computing system. "I think the basics of quantum computing is quantum physics. Quantum physics clearly shows there is always this inflection point and then after that, either the current hardware or the quant developers will be able to suddenly create magic. My only personal belief is that the pressure on the systems will come in because of the data explosion," he said.

People now see social media sites as their sole source of information due to their popularity. The Majority of people get their news through social media. At the same time, fake news has grown exponentially on social media platforms in recent years. Several artificial intelligence-based solutions for detecting fake news have shown promising results. On the other hand, these detection systems lack explanation capabilities, i.e., the ability to explain why they made a prediction. This paper highlights the current state of the art in explainable fake news detection. We discuss the pitfalls in the current explainable AI-based fake news detection models and present our ongoing research on multi-modal explainable fake news detection model.