Transparency and explainability in ML training and decision-making, as well as these models' effects on employment and societal structures, are areas for ongoing oversight and discussion.What are the different types of machine learning? Classical ML is often categorized by how an algorithm lea...
Including only the most relevant features means less complexity, which is good not only for model explainability, but also for training speed. Less complexity can also mean better accuracy, as irrelevant features introduce noise. Reduces overfitting. Overfitting is when a model has learned too much...
Training a machine learning model is an iterative process, and does not always guarantee a robust model. Using an inference model can provide better performance compared to an in-house model. Nowadays, model explainability and bias mitigation are crucial, and inference models may need to be ...
spending behavior, or hidden correlations influenced the decision. Financial institutions must balance accuracy with explainability, ensuring compliance with regulatory standards like General Data Protection Regulation (GDPR). This has fueled demand forexplainable AI (XAI)to make deep learning more...
In the context of machine learning and artificial intelligence, explainability is the ability to understand “the ‘why’ behind the decision-making of the model,” according to Joshua Rubin, director of data science at Fiddler AI. Therefore, explainable AI requires “drilling into” the model in...
Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?Machine Learning (ML)AISecure MLExplainable MLFairnessRecent advances in machine learning (ML) and the surge in computational power have opened the way to the proliferation of ML ...
Explainability versus interpretability in AI Interpretability is the degree to which an observer can understand the cause of a decision. It is the success rate that humans can predict for the result of an AI output, while explainability goes a step further and looks at how the AI arrived at ...
For example, transfer learning takes human feedback on one task and applies it to other similar tasks. Explainability. RLHF will help advance explainable AI efforts, providing more transparent outputs and explaining the steps they performed when training models. New regulations. AI governance ...
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在第一章中,本文将介绍机器学习的可信性(Trustworthiness),包含可解释性(Explainability)、公平性(Fairness)、隐私性(Privacy)和鲁棒性(Robustness)。其中可解释性是最重要的,因为它在一定程度上可以促进后三者。因此,本文重点关注机器学习的可解释性。 在第二章中,本文将详细阐述什么是可解释性(what),为什么需要可解...