Antwort What is explainable AI basics? Weitere Antworten – What is the basics of explainable AI

What is explainable AI basics?
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. Explainable AI is crucial for an organization in building trust and confidence when putting AI models into production.We have termed these four principles as explanation, meaningful, explanation accuracy, and knowledge limits, respectively.For example, hospitals can use explainable AI for cancer detection and treatment, where algorithms show the reasoning behind a given model's decision-making. This makes it easier not only for doctors to make treatment decisions, but also provide data-backed explanations to their patients.

How do you simply explain AI : Artificial Intelligence (AI) is an evolving technology that tries to simulate human intelligence using machines. AI encompasses various subfields, including machine learning (ML) and deep learning, which allow systems to learn and adapt in novel ways from training data.

Is ChatGPT an explainable AI

ChatGPT is a non-explainable AI, and if you ask questions like “The most important EU directives related to ESG”, you will get completely wrong answers, even if they look like they are correct.

Do we really need explainable AI : Understanding an AI's decision process is crucial for identifying potential risks and devising strategies to mitigate them. In summary, Explainable Acritical Intelligence is essential to enable transparent, ethical, and effective AI applications in various domains.

The five AI ethical principles, based on recommendations from the Defense Innovation Board, are:

  • Responsible. DOD personnel will exercise appropriate levels of judgment and care while remaining responsible for the development, deployment and use of AI capabilities.
  • Equitable.
  • Traceable.
  • Reliable.
  • Governable.


explainable AIs solve this

We define XAI based on 5 pillars- Explanation, Accuracy, Relevance, Boundaries, and Feedback.

What are the 4 types of AI with example

4 main types of artificial intelligence

  • Reactive machines. Reactive machines are AI systems that have no memory and are task specific, meaning that an input always delivers the same output.
  • Limited memory machines. The next type of AI in its evolution is limited memory.
  • Theory of mind.
  • Self-awareness.

John McCarthy

John McCarthy is considered as the father of Artificial Intelligence. John McCarthy was an American computer scientist.Roko's basilisk states that humanity should seek to develop AI, with the finite loss becoming development of AI and the infinite gains becoming avoiding the possibility of eternal torture. However, like its parent, Roko's basilisk has widely been criticized.

Explainability techniques

  • SHAP.
  • LIME.
  • Permutation Importance.
  • Partial Dependence Plot.
  • Morris Sensitivity Analysis.
  • Accumulated Local Effects (ALE)
  • Anchors.
  • Contrastive Explanation Method (CEM)

What is the difference between generative AI and Explainable AI : Comparison. Purpose: Generative AI aims at creating new data or content, while Explainable AI aims at making the operations of AI systems transparent and understandable. Technological Focus: Generative AI leverages advanced models like GANs and transformers to generate new content.

Why is XAI necessary : AI models can inadvertently learn biases present in the training data, leading to unfair or discriminatory decisions. XAI tools can help identify these biases and provide insights into why certain decisions may favor certain groups, allowing for bias mitigation and fairness enhancement.

What are the main 7 areas of AI

In this article, we'll go over the main branches of artificial intelligence, such as:

  • Computer vision.
  • Fuzzy logic.
  • Expert systems.
  • Robotics.
  • Machine learning.
  • Neural networks/deep learning.
  • Natural language processing.


✓ Ensure that the development, deployment and use of AI systems meets the seven key requirements for Trustworthy AI: (1) human agency and oversight, (2) technical robustness and safety, (3) privacy and data governance, (4) transparency, (5) diversity, non-discrimination and fairness, (6) environmental and societal well …Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language.

What are the three 3 key elements for AI : AI is made up of various technologies, including Machine Learning, Natural Language Processing, and Robotics.