Explainable AI
Explainable AI refers to the branch of artificial intelligence (AI) that emphasizes the development of algorithms and models that can be understood, interpreted, and explained by human beings. This is in contrast to many traditional AI approaches, which focus on creating black-box models that may achieve high levels of accuracy but are difficult for humans to interpret or understand. Explainable AI seeks to create models that are transparent, explainable, and trustworthy, allowing users to better understand how decisions are made and to build confidence in the technology. This is particularly important in applications where AI systems are used to make critical decisions, such as in healthcare, finance, and safety-critical systems.
Child Hierarchical Categories
[AI Accountability]
[AI Bias]
[AI Policy]
[AI Security]
[AI Transparency]
[Artificial Intelligence Technologies]
[Artificial Neural Networks]
[Computer Vision]
[Cybernetics]
[Decision Trees]
[Deep Learning]
[Evolutionary Computation]
[Expert Systems]
[Explainable AI Algorithms]
[Fuzzy Logic]
[Genetic Algorithms]
[KNearest Neighbors]
[Logistic Regression]
[Machine Learning]
[Naive Bayes]
[Natural Language Processing]
[Neural Networks]
[Pattern Recognition]
[Principal Component Analysis]
[Reinforcement Learning]
[Robotics]
[Support Vector Machines]
External Links
- [ExplainableAI.com]
- [qios.us] HMX | Explainable AI | Human + Machine Either Alone
- [alpha-quantum.com] Machine Learning Solutions - Alpha Quantum, Data Science, Explainable AI