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.

We study the computational complexity of abstract argumentation semantics based on  weak admissibility, a recently introduced concept to deal with arguments of self-defeating  nature. Our results reveal that semantics based on weak admissibility are of much higher  complexity (under typical assumptions) compared to all argumentation semantics which  have been analysed in terms of complexity so far. In fact, we show PSPACE-completeness  of all non-trivial standard decision problems for weak-admissible based semantics. We then  investigate potential tractable fragments and show that restricting the frameworks under  consideration to certain graph-classes significantly reduces the complexity. We also show  that weak-admissibility based extensions can be computed by dividing the given graph into  its strongly connected components (SCCs). This technique ensures that the bottleneck  when computing extensions is the size of the largest SCC instead of the size of the graph  itself and therefore contributes to the search for fixed-parameter tractable implementations  for reasoning with weak admissibility. 

Explanation mechanisms from the field of Counterfactual Thinking are a widely-used paradigm for Explainable Artificial Intelligence (XAI), as they follow a natural way of reasoning that humans are familiar with. However, all common approaches from this field are based on communicating information about features or characteristics that are especially important for an AI's decision. We argue that in order to fully understand a decision, not only knowledge about relevant features is needed, but that the awareness of irrelevant information also highly contributes to the creation of a user's mental model of an AI system. Therefore, we introduce a new way of explaining AI systems. Our approach, which we call Alterfactual Explanations, is based on showing an alternative reality where irrelevant features of an AI's input are altered. By doing so, the user directly sees which characteristics of the input data can change arbitrarily without influencing the AI's decision. We evaluate our approach in an extensive user study, revealing that it is able to significantly contribute to the participants' understanding of an AI. We show that alterfactual explanations are suited to convey an understanding of different aspects of the AI's reasoning than established counterfactual explanation methods.

Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc. As the AI debate attracts more attention these years, it is worth exploring the methods to automate the tedious process involved in the debating system. In this work, we introduce a comprehensive and large dataset named IAM, which can be applied to a series of argument mining tasks, including claim extraction, stance classification, evidence extraction, etc. Our dataset is collected from over 1k articles related to 123 topics. Near 70k sentences in the dataset are fully annotated based on their argument properties (e.g., claims, stances, evidence, etc.). We further propose two new integrated argument mining tasks associated with the debate preparation process: (1) claim extraction with stance classification (CESC) and (2) claim-evidence pair extraction (CEPE). We adopt a pipeline approach and an end-to-end method for each integrated task separately. Promising experimental results are reported to show the values and challenges of our proposed tasks, and motivate future research on argument mining.

Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature. To shed light on these black-box models, previous works predominantly focus on identifying the contribution of input features to the diagnosis, i.e., feature attribution. In this work, we explore counterfactual explanations to identify what patterns the models rely on for diagnosis. Specifically, we investigate the effect of changing features within chest X-rays on the classifier's output to understand its decision mechanism. We leverage a StyleGAN-based approach (StyleEx) to create counterfactual explanations for chest X-rays by manipulating specific latent directions in their latent space. In addition, we propose EigenFind to significantly reduce the computation time of generated explanations. We clinically evaluate the relevancy of our counterfactual explanations with the help of radiologists. Our code is publicly available.

Modern Artificial Intelligence (AI) enabled Intrusion Detection Systems (IDS) are complex black boxes. This means that a security analyst will have little to no explanation or clarification on why an IDS model made a particular prediction. A potential solution to this problem is to research and develop Explainable Intrusion Detection Systems (X-IDS) based on current capabilities in Explainable Artificial Intelligence (XAI). In this paper, we create a Self Organizing Maps (SOMs) based X-IDS system that is capable of producing explanatory visualizations. We leverage SOM's explainability to create both global and local explanations. An analyst can use global explanations to get a general idea of how a particular IDS model computes predictions. Local explanations are generated for individual datapoints to explain why a certain prediction value was computed. Furthermore, our SOM based X-IDS was evaluated on both explanation generation and traditional accuracy tests using the NSL-KDD and the CIC-IDS-2017 datasets.