AIISCIn the age of Industry 4.0 and smart automation, unplanned downtime costs industries over $50 billion annually. Even with preventive maintenance, industries like automotive lose more than $2 million per hour due to downtime caused by unexpected or “rare” events. The extreme rarity of these events makes their detection and prediction a significant challenge for AI practitioners. Factors such as the lack of high-quality data, methodological gaps in the literature, and limited practical experience with multimodal data exacerbate the difficulty of rare event detection and prediction. This lab will provide hands-on experience to learn how to address these challenges by exploring the entire lifecycle of rare event analysis, from data generation and preprocessing to model development and evaluation. Developing a process ontology for user-level/application-level/domain-specific explanations will also be demonstrated. Participants will be introduced to the limited publicly available datasets and, will gain hands-on experience with a newly developed multi-modal dataset designed explicitly for rare event prediction. Through several hands-on sessions, participants will learn how to generate such a high-quality dataset and the practical use of this dataset to develop rare event prediction models. Those interested in developing AI models involving diverse multimodal data for other applications will also benefit from participation. The learning from this lab will also be relevant to other domains and applications, such as healthcare, finance, and energy, where predictive maintenance can help prevent costly failures in complex systems. Participants will gain valuable insights and skills transferrable across industries where rare events impact operational efficiency and require advanced predictive techniques.
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Large Language Models (LLMs) are credible with open-domain interactions such as question answering, summarization, and explanation generation. LLM reasoning is based on parametrized knowledge, and as a consequence, the models often produce absurdities and inconsistencies in outputs (eg, hallucinations and confirmation biases)[2]. In essence, they are fundamentally hard to control to prevent off-the-rails behaviors, are hard to fine-tune, customize for tailored needs, prompt effectively (due to the “tug-of-war” between external and parametric memory), and extremely resource-hungry due to the enormous size of their extensive parametric configurations. Thus, significant challenges arise when these models are required to perform in critical applications in domains such as healthcare and finance, that need better guarantees and in turn, need to support grounding, alignment, and instructibility. AI models for such critical applications should be customizable or tailored as appropriate for supporting user assistance in various tasks, compact to perform in real-world resource-constraint settings, and capable of controlled, robust, reliable, interpretable, and grounded reasoning (grounded in rules, guidelines, and protocols). This special session explores the development of compact, custom neurosymbolic AI models and their use through human-in-the-loop co-pilots for use in critical applications.
Improving the performance and explanations of ML algorithms is a priority for adoption by humans in the real world. In critical domains such as healthcare, such technology has significant potential to reduce the burden on humans and considerably reduce manual assessments by providing quality assistance at scale. In today’s data driven world, artificial intelligence (AI) systems are still experiencing issues with bias, explainability, and human-like reasoning and interpretability. Causal AI is the technique that can reason and make human like choices making it possible to go beyond narrow Machine learning based techniques and can be integrated into human decision making. It also offers intrinsic explainability, new domain adaptability, bias free predictions and works with datasets of all sizes. In this tutorial of type lecture style we detail how a richer representation of causality in AI systems using a knowledge graph (KG) based approach is needed for intervention and counterfactual reasoning (Figure 1), how do we get to model based and domain explainability, how causal representations helps in web and health care.
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) condi- tions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, to predict MH condi- tions. Subsequently, researchers created classification schemes to measure the severity of each MH condition. Such ad-hoc schemes, engineered features, and models not only require a large amount of data but fail to allow clinically acceptable and explainable reasoning over the outcomes. To improve Neural-AI for MHCare, infusion of clinical symbolic knowledge that clinicans use in decision mak- ing is required. An impactful use case of Neural-AI systems in MH is conversational systems. These systems require coordination between classification and generation to facilitate humanistic con- versation in conversational agents (CA). Current CAs with deep language models lack factual correctness, medical relevance, and safety in their generations, which intertwine with unexplainable statistical classification techniques.
Autonomous Driving (AD) is considered as a testbed for tackling many hard AI problems. Despite the recent advancements in the field, AD is still far from achieving full autonomy due to core technical problems inherent in AD. The emerging field of neuro-symbolic AI and the methods for knowledge-infused learning are showing exciting ways of leveraging external knowledge within machine/deep learning solutions, with the potential benefits for interpretability, explainability, robustness, and transferability. In this tutorial, we will examine the use of knowledge-infused learning for three core state-of-the-art technical achievements within the AD domain. With a collaborative team from both academia and industry, we will demonstrate recent innovations using real-world datasets.
For more information, visit the KL4AD website.
Virtual health agents (VHAs) have received considerable attention, but the early focus has been on collecting data, helping patients follow generic health guidelines, and providing reminders for clinical appointments. While presenting the collected data and frequency of visits to the clinician is useful, further context and personalization are needed for a VHA to interpret and understand what the data means in clinical terms. This has made their use in managing health limited. Such understanding enables patient empowerment and self-appraisal -- i.e., aiding the patient in interpreting the data to understand the changes in the patient’s health conditions, and self-management -- i.e., to help a patient better manage their health through better adherence to the clinician guidelines and clinician recommended care plan. Crisis conditions such as the current pandemic have further stressed our healthcare system and have made the need for such advanced support more attractive and in demand. Consider the rapid growth in mental health because the patients who already had mental health conditions worsen, and many develop such conditions due to the challenges arising from lockdown, isolation, and economic hardships. The severe lack of timely availability of clinical expertise to meet the rapidly growing demand provides the motivation for advancing this research in developing more advanced VHAs and evaluating it in the context of mental health management.
For more information, visit the KiRL website.
During the last decade, traditional data-driven deep learning (DL) has shown remarkable success in essential natural language processing tasks, such as relation extraction. Yet, challenges remain in developing artificial intelligence (AI) methods in real-world cases that require explainability through human interpretable and traceable outcomes. The scarcity of labeled data for downstream supervised tasks and entangled embeddings produced as an outcome of self-supervised pre-training objectives also hinders interpretability and explainability. Additionally, data labeling in multiple unstructured domains, particularly healthcare and education, is computationally expensive as it requires a pool of human expertise. Consider Education Technology, where AI systems fall along a “capability spectrum” depending on how extensively they exploit various resources, such as academic content, granularity in student engagement, academic domain experts, and knowledge bases to identify concepts that would help achieve knowledge mastery for student goals. Likewise, the task of assessing human health using online conversations raises challenges for current statistical DL methods through evolving cultural and context-specific discussions. Hence, developing strategies that merge AI with stratified knowledge to identify concepts that would delineate healthcare conversations patterns and help healthcare professionals decide. Such technological innovations are imperative as they provide consistency and explainability in outcomes. This tutorial discusses the notion of explainability and interpretability through the use of knowledge graphs in (1) Healthcare on the Web, (2) Education Technology. This tutorial will provide details of knowledge-infused learning algorithms and its contribution to explainability for the above two applications that can be applied to any other domain using knowledge graphs.
For more information, visit https://aiisc.ai/xaikg/
Recent advances in statistical and data-driven deep learning demonstrate significant success in natural language understanding without using prior knowledge, especially in structured and generic domains, where data is abundant. On the other hand, in text processing problems that are dynamic and impact the society at large, existing data-dependent, state-of-the-art deep learning methods remain vulnerable to veracity considerations and especially, high volume that masks small, emergent signals. Statistical natural language processing methods have shown poor performance in capturing: (1) Human well being online especially in evolving events (e.g. mental health communications on Reddit, Twitter), (2) Culture and context specific discussion on the web (e.g. humor detection, extremism on social media), (3) Social Network Analysis (help-seeker and care-provider) during pandemic or disaster scenarios, and (4) Explainable methods of learning that drive technological innovations and inventions for community betterment. In such social hypertext, leveraging the semantic-web concept of knowledge graphs is a promising approach to the enhancement of deep learning and natural language processing.
According to Piagetian human learning theory, the activation of existing schema guides the apprehension of experience to support the generation of context sensitive responses. Activating prior knowledge connects current and past experience for identifying relations, supporting explanation, reducing ambiguity, structuring new knowledge, and application to novel materials. Further, human learning does not necessarily rely on large amounts of (annotated) cases to proceed. Because prior knowledge is so powerful in human learning, its incorporation at various levels of abstraction in deep learning could benefit outcomes. Example the desiderata include compensating for data limitations, improving inductive bias, generating explainable outcomes and enabling trust. These are particularly useful for data-limited but otherwise complex, evolving problems in domains such as mental healthcare, online social threats and epidemic/pandemic.
Despite the general agreement that structured prior knowledge and tacit knowledge (the inferred outcome of a model) resulting from deep learning should be combined, there has been little progress. Recent debates on Neuro-Symbolic AI , the inclusion of innate priors in deep learning, and AI fireside chat have identified knowledge-infused learning to improve explainability, interpretability, and trust in AI systems.
In this tutorial, we take use cases from the aforementioned two social good applications (Mental Health, Radicalization) and multimodal aspects of social media (e.g. scene understanding from images, video and text (hypermedia/hypertext) often found in documentation of critical events to explore the modern aspect of hypertext using semantic web in the form of Knowledge Graphs (KG). Specifically, the tutorial will provide a detailed walkthrough on Knowledge Graphs and their utility in developing knowledge-infusion techniques for interpretable and explainable learning for text, video, images, and graphical data on the web with the following agenda: Motivate the novel paradigm of knowledge-infused learning using computational learning and cognitive theories. Describe the different forms of knowledge, methods of automatic modeling of KG, and infusion methods in deep/machine learning. Discuss application-specific evaluation methods specifically for explainability and reasoning using benchmark datasets and knowledge-resources that show promise in advancing the capabilities of deep learning. Future directions of KGs and robust learning for the Web and Society.
In today's data-driven world, organizations derive insights from massive amounts of data through large scale statistical machine learning models. However, statistical techniques can be easy to fool with adversarial instances (a neural network can predict a non-extremist as an extremist by mere presence of the word Jihad), which raises question in Data Quality. In high stakes decision making problems, such as cyber social threats, it is highly sensitive to classify a non-extremist as an extremist and vice-versa. Data quality is good if the data possesses adequate domain coverage and the labels contain adequate semantics. For example, is the semantics of an extremist vs. non-extremist vis-a-vis the word Jihad captured in the label (adequate semantics in labels)? Also, are there enough non-extremists with the word Jihad in the training data from the perspective of religion, hate, or ideology? Thus semantic annotation of the data, beyond mere labels attached to data instances, can significantly improve the robustness of model outcomes and ensure that the model has learned from trustworthy, knowledge-guided data standards. It is important to note that the knowledge-guided standards help de-bias the data if specified correctly (contextualized de-biasing extremist behavior data from bias towards the word Jihad). Therefore, in addition to trust in the robustness of outcomes, knowledge guided data creation also enables fair and ethical practices during real-world deployment of machine learning in high stakes decision making. We denote such data as Explainable Data. In this tutorial of type course and case-studies, we detail how to construct Explainable Data using various expert resources and knowledge graphs. All the materials (resources and implementations) presented during the tutorial will be made available on: KIWO-ICWSM, a week before the tutorial. We plan a 90 minute tutorial (Intermediate Level) with 2 breaks (5 mins each).