Neural network interpretability via counterfactual analysis
Counterfactual analysis is a new approach to post-hoc interpretability, which is based on the idea of generating counterfactual instances that are similar to the original instance but lead to different model predictions.
SHAP and LIME: A survey and experimental comparison
SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are two popular methods for explaining individual predictions of black box models.
Explainable by Design: Neural Module Networks
Neural module networks (NMNs) are a class of neural network architectures designed to be interpretable by humans. They achieve this by decomposing complex tasks into a sequence of simple, modular subtasks, each of which has a clear and intuitive interpretation.
A survey of explainable AI in healthcare
Explainable AI (XAI) is an emerging field that aims to make machine learning models more transparent and interpretable. This is particularly important in healthcare, where the decisions made by AI systems can have life-or-death implications.
Explaining AI in real time with adaptive explanations
Adaptive explanations are a new approach to AI explainability that takes into account the user's mental model and provides explanations that are tailored to their level of expertise and the context of the interaction. This can help bridge the gap between the internal logic of AI systems and the user's understanding of them.
Neuro-Symbolic AI: Integrating Deep Learning with Symbolic Reasoning
Neuro-symbolic AI is an emerging research area that aims to combine the strengths of deep learning and symbolic reasoning to create more powerful and interpretable AI systems. This approach has the potential to overcome some of the limitations of purely data-driven methods and enable AI systems to exhibit more human-like intelligence.
Explainable AI in finance: Opportunities and challenges
Explainable AI (XAI) is an emerging field that aims to make machine learning models more transparent and interpretable. This is particularly important in finance, where the decisions made by AI systems can have significant financial and regulatory implications.
The role of explainable AI in medical diagnosis
Explainable AI (XAI) is an emerging field that aims to make machine learning models more transparent and interpretable. This is particularly important in medical diagnosis, where the decisions made by AI systems can have life-or-death implications.