Price: $175.00 – $152.42
(as of Dec 26,2024 13:24:14 UTC – Details)
Publisher : The Institution of Engineering and Technology (December 5, 2023)
Language : English
Hardcover : 530 pages
ISBN-10 : 1839536950
ISBN-13 : 978-1839536953
Item Weight : 2 pounds
Dimensions : 6.4 x 1.4 x 9.2 inches
Explainable Artificial Intelligence (XAI): Concepts, enabling tools, technologies and applications (Computing and Networks)
Artificial Intelligence (AI) has made significant advancements in recent years, with algorithms and models becoming increasingly complex and accurate. However, as AI systems become more sophisticated, they also become more opaque and difficult to interpret. This lack of transparency has led to concerns about the reliability and trustworthiness of AI systems, especially in critical applications such as healthcare, finance, and autonomous vehicles.
Explainable Artificial Intelligence (XAI) aims to address this issue by making AI systems more transparent and interpretable. XAI techniques enable users to understand how AI models make decisions, providing insight into the underlying reasoning and logic. This not only helps build trust in AI systems but also allows users to identify and correct biases, errors, and other issues that may arise.
There are several key concepts and enabling tools in XAI that help achieve transparency and interpretability in AI systems. Some of these include:
1. Model-agnostic methods: These techniques can be applied to any machine learning model, allowing users to gain insights into the decision-making process without needing to understand the specific details of the model.
2. Local and global explanations: Local explanations focus on explaining individual predictions, while global explanations provide an overview of how the model behaves overall.
3. Feature importance and impact analysis: These methods help identify which features are most influential in driving the model’s predictions, allowing users to understand the underlying factors that contribute to the decision-making process.
4. Visualization tools: Visualizations can help users interpret complex AI models by representing the data and decision-making processes in a more intuitive and understandable way.
In terms of technologies, XAI is enabled by a variety of tools and frameworks, such as LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and TensorBoard. These tools provide users with the means to explain and interpret AI models in a more transparent and accessible manner.
Applications of XAI are wide-ranging and diverse, spanning various industries and domains. In healthcare, XAI can help doctors and researchers understand how AI models arrive at medical diagnoses, providing valuable insights into patient outcomes and treatment recommendations. In finance, XAI can be used to explain credit scoring models and investment decisions, helping to mitigate risks and improve decision-making processes. In autonomous vehicles, XAI can help ensure the safety and reliability of AI systems by providing explanations for driving decisions and actions.
Overall, XAI plays a crucial role in ensuring the transparency and trustworthiness of AI systems, enabling users to understand and interpret the decisions made by AI models. By leveraging XAI concepts, enabling tools, technologies, and applications, we can unlock the full potential of AI while ensuring that it remains accountable, reliable, and ethical in all its applications.
#Explainable #Artificial #Intelligence #XAI #Concepts #enabling #tools #technologies #applications #Computing #Networks
