Wall Street titans warn a recession is imminent but its not that simple. Here are the warning signs you need to understand.

Advances in AI, especially based on machine learning, have provided a powerful way to extract useful patterns from large, heterogeneous data sources. The rise in massive amounts of data, coupled with powerful computing capabilities, makes it possible to tackle previously intractable real-world problems. Medicine, business, government, and science are rapidly automating decisions and processes using machine learning. Unlike traditional AI approaches based on explicit rules expressing domain knowledge, machine learning often lacks explicit human-understandable specification of the rules producing model outputs. With growing reliance on automated decisions, an overriding concern is understanding the process by which "black box" AI techniques make decisions.

Hospice is a compassionate approach focusing on quality of life for terminally ill patients and their caregivers, with approximately 1.55 million Medicare beneficiaries enrolled in hospice care for at least one day during 2018 – 17% more than in 2014. However, at least 14% of Medicare beneficiaries enrolled in hospice stayed for more than 180 days, and hospice stays beyond six months can result in substantial excess costs to healthcare organizations under value-based care arrangements. David Klebonis, COO of Palm Beach Accountable Care Organization, has developed highly interpretable machine learning models that, because of the sensitivity of the clinical decision involved, cannot only accurately predict hospice overstays to drive appropriate hospice referrals, but also surface decision criteria that satisfy clinician scrutiny and promote adoption. "Artificial intelligence and machine learning have the potential to use data to predict patients with a high probability of expiring within the next six months, so that physicians can enter into conversations with these patients and their families about the possibility of referral to hospice," he said. Klebonis, who will address the topic this month at HIMSS22, said in Florida about 58% of Medicare decedents were in hospice at the time of death.

As artificial intelligence technologies have gained popularity, creators and users have used and modified a variety of terms and phrases to describe and characterize their work. One may probably have heard terms like "narrow AI", "deep learning", "neural networks", and other new descriptors being used to distinguish between distinct types and roles of AI, different sections of AI solutions, and so on. Another result of the rapid expansion of AI's availability and use has been the demand for "explainable AI," or approaches and procedures that make Artificial Intelligence more accessible to all types of humans. And, while explainable AI is important, it's not really the answer always. The ambition to understand how computers make decisions is admirable, but XAI technologies or approaches will never be sufficient.

The application has some limitations, and it doesn't implement all of the capabilities of LIME. We'll cover the limitations of our app in more detail below.

AI is astonishing: it can drive cars, answer questions, match people's faces with their passport photos, beat the best champions at chess, and much more… But, have you ever wondered how it works? And what happens when it makes mistakes? Will it ever become dangerous? We are far from Terminator-like catastrophic events, but the problem is real. AI now competes and sometimes outperforms people on many tasks thanks to the development of new learning algorithms, particularly neural networks.

Importance of XAI in modern world Differentiation of glass box, white box and black box ML models Categorization of XAI on the basis of their scope, agnosticity, data types and explanation techniques Trade-off between accuracy and interpretability Application of InterpretML package from Microsoft to generate explanations of ML models Need of counterfactual and contrastive explanations Working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanationss Application of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets. Application of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets. This course provides detailed insights into the latest developments in Explainable Artificial Intelligence (XAI). Our reliance on artificial intelligence models is increasing day by day, and it's also becoming equally important to explain how and why AI makes a particular decision. Recent laws have also caused the urgency about explaining and defending the decisions made by AI systems.

Explainable AI refers to strategies and procedures used in the application of artificial intelligence (AI) that allow human specialists to understand the solution's findings. Explainable AI refers to strategies and procedures used in the application of artificial intelligence (AI) that allow human specialists to understand the solution's findings. To ensure that explanation methods are correct, they must be systematically reviewed and compared. In contrast to achieving quantitative explanation, in this article, we will discuss Quantus, a Python library that evaluates a convolutional neural network's working, predictions and explanation of parameters. Below is the list of major points that will be discussed in this article.

In this article, we will go to see together the main methods used for explainable AI (SHAP, LIME, Tree surrogates etc.), and their characteristics.

"Explainable AI (XAI) is a set of methods aimed at making increasingly complex Machine Learning (ML) models understandable by humans". That's how I defined XAI in a previous post where I argued that XAI is both important and extremely difficult to automate. In a nutshell, XAI is crucial for building trust and understanding with (often non-technical) end-users. This empowers the user to actively use and adapt the system. The goal is to create ML-systems with maximal benefits and minimal accidental misuse.