Exploring the ethical dimensions of artificial intelligence (AI) reveals the unique position AI systems hold in behaving ethically. From causality to interpretability, bias, and privacy concerns, this discussion sheds light on the importance of ethical considerations in AI development and implementa

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Explore how to improve interpretability of AI models through generating counterfactual explanations with minimal perturbations and realistic, actionable suggestions. Addressing limitations of current methods and the need for a more flexible generative framework to ensure explanations are clear, conc

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Large Language Models (LLMs) are sophisticated deep learning algorithms capable of understanding and generating human language. These models, trained on massive datasets, excel at various natural language processing tasks such as sentiment analysis, text classification, natural language inference, s

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Delve into the concept of unconditional counterfactual explanations without revealing the black box of AI decision-making processes. Discover how these explanations aim to inform, empower, and provide recourse for individuals impacted by automated decisions, addressing key challenges in interpretabi

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Statistics, as Florence Nightingale famously said, is the most important science in the world. In this chapter on logistic regression, we delve into model selection, interpretation of parameters, and methods such as forward selection, backward elimination, and stepwise selection. Guidelines for sele

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Delve into the realm of mechanistic interpretability in neural networks, exploring how models can learn human-comprehensible algorithms and the importance of deciphering internal features and circuits to predict and align model behavior. Discover the goal of reverse-engineering neural networks akin

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This article explores the concept of local maladies in homeopathy, focusing on the treatment of diseases affecting external parts of the body. It delves into Hahnemann's aphorisms, emphasizing the interconnectedness of local afflictions with the overall health of the body. Case-taking plays a crucia

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Unified methodology for interpreting model predictions through additive explanations and Shapley values. It discusses the relationship between Additive Explanations and LIME, introduces Shapley values, approximations, experiments, and extensions in model interpretation. The approach unifies various

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Interpretability in machine learning models is crucial in medicine for clear decision-making and validation by medical professionals. This article discusses the definitions, global and local explanations, tradeoffs between interpretability and accuracy, and reasons why interpretable models are essen

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Local winds play a vital role in the climate of specific regions, influenced by factors like nearby bodies of water or mountain ranges. Different types of local winds, such as Foehn, Sirocco, Chinook, Loo, Mistral, and Bora, have unique characteristics and impacts on the environment. For example, th

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Ecotourism promotes responsible travel to natural areas that conserves the environment and benefits local communities. It focuses on maintaining ecosystems, protecting wildlife, preserving local culture and history, and blending infrastructure with the environment. Standards include protection of ec

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Collaboration between the Local Government Advisory Committee (LGAC) and the Local Leadership Workgroup (LLWG) focuses on engaging with local decision-makers, implementing strategies for local engagement, and supporting the flow of information among local governments in the Chesapeake Bay watershed

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This collection of images and descriptions dives into the realm of Vector Space Models (VSMs) and their composition, focusing on how to make a VSM, previous work in the field, matrix factorization, interpretability of latent dimensions, and utilizing SVD for interpretability. The research addresses

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Factor analysis involves rotation of the factor loading matrix to enhance interpretability. This process was originally done manually but is now performed analytically with computers. Factors can be orthogonal or oblique, impacting the interpretation of factor loadings. Understanding rotation simpli

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Local and Regional Procurement (LRP) initiatives involve sourcing food aid locally to reduce delivery costs, delays, and market distortions. They aim to provide economic opportunities for small farmers while addressing potential negative impacts on local markets. Strategies such as cash transfers an

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Local content policies in the oil, gas, and mining sectors aim to maximize the use of local resources, services, and participation for economic development. Definitions and objectives of local content vary, impacting policy outcomes and stakeholder priorities. This overview explores the significance

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This project aims to provide a comprehensive medical imaging dataset for two-stage transfer learning, facilitating the evaluation of architectures utilizing this approach. Transfer learning in medical imaging involves adapting pre-trained deep learning models for specific diagnostic tasks, enhancing

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IEEE 802c defines Local Medium Access Control (MAC) Address Usage, providing structured approaches through the Structured Local Address Plan (SLAP) in different regions of the local MAC address space. Local MAC addresses are not globally unique, and the assignment is within local administration's sc

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In the realm of cybersecurity, the perpetual battle between security analysts and adversaries intensifies with the increasing complexity of cyber attacks. Machine learning (ML) is increasingly utilized to combat these challenges, but vulnerable to adversarial attacks. Investigating defenses against

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The paper discusses evaluating interpretability in Machine Learning by examining human-simulatability complexity and the relationship between decision set complexity and interpretability. It explores different factors affecting interpretability through user studies and highlights the significance of

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Multivariate Adaptive Regression Splines (MARS) offer a flexible approach in machine learning by combining features of linear regression, non-linear regression, and basis expansions. Unlike traditional models, MARS makes no assumptions about the underlying functional relationship, leading to improve

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