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.

Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making. SHAP (SHapley Additive exPlanations) is a framework that explains the output of any model using Shapley values, a game-theoretic approach often used for optimal credit allocation. While this can be used on any black-box model, SHAP can compute more efficiently on specific model classes (like tree ensembles).

Can your brand explain how its artificial intelligence (AI) applications work, and why they make the decisions they do? Brand trust is hard to win and easy to lose, and transparent and easily explainable AI applications are a great start towards building customers' trust and enhancing the efficiency and effectiveness of AI apps. This article looks at Explainable AI (XAI), and why it should be a part of your brand's AI strategy. Typical AI apps are often referred to as "black box" AI because whatever occurs within the application is relatively unknown to all but those data scientists, programmers and designers who created it. Individually, even those people may not be able to explain anything outside of their primary domain.

More than two dozen artificial intelligence experts from business and academia, including Texas McCombs, explored the importance of understanding how machine learning systems arrive at their conclusions so humans can trust those results. Although AI is more than 50 years old, "deep learning has been a mini-scientific revolution" since the 2010s, said one keynote speaker, Charles Elkan, a professor of computer science at the University of California, San Diego. Alice Xiang, a lawyer and a senior research scientist for Sony Group, said, "I see explainability as an important part of providing transparency and, in turn, enabling accountability." She noted the challenge of black boxes, citing as examples drug-sniffing dogs, whose abilities are mysterious but highly accurate, and the horse Clever Hans, who appeared to understand math but was really following cues from its owner. In a panel discussion called "Adopting AI," James Guszcza, a behavioral research affiliate at Stanford University and chief data scientist on leave from Deloitte LLP, said: "I think one of the previous speakers said we need to be interdisciplinary; I take it a little bit further and say we need to be transdisciplinary."

While machine learning models are usually assumed to always output a prediction, there also exist extensions in the form of reject options which allow the model to reject inputs where only a prediction with an unacceptably low certainty would be possible. With the ongoing rise of eXplainable AI, a lot of methods for explaining model predictions have been developed. However, understanding why a given input was rejected, instead of being classified by the model, is also of interest. Surprisingly, explanations of rejects have not been considered so far. We propose to use counterfactual explanations for explaining rejects and investigate how to efficiently compute counterfactual explanations of different reject options for an important class of models, namely prototype-based classifiers such as learning vector quantization models.

Accurate electricity demand forecasts play a crucial role in sustainable power systems. To enable better decision-making especially for demand flexibility of the end-user, it is necessary to provide not only accurate but also understandable and actionable forecasts. To provide accurate forecasts Global Forecasting Models (GFM) trained across time series have shown superior results in many demand forecasting competitions and real-world applications recently, compared with univariate forecasting approaches. We aim to fill the gap between the accuracy and the interpretability in global forecasting approaches. In order to explain the global model forecasts, we propose Local Interpretable Model-agnostic Rule-based Explanations for Forecasting (LIMREF), a local explainer framework that produces k-optimal impact rules for a particular forecast, considering the global forecasting model as a black-box model, in a model-agnostic way. It provides different types of rules that explain the forecast of the global model and the counterfactual rules, which provide actionable insights for potential changes to obtain different outputs for given instances. We conduct experiments using a large-scale electricity demand dataset with exogenous features such as temperature and calendar effects. Here, we evaluate the quality of the explanations produced by the LIMREF framework in terms of both qualitative and quantitative aspects such as accuracy, fidelity, and comprehensibility and benchmark those against other local explainers.