Tag Archives: explainable AI

Explainable, Interpretable, and Transparent AI Systems

Leave a reply


Price: $4.99
(as of Dec 27,2024 02:18:44 UTC – Details)




ASIN ‏ : ‎ B0D8L4Z312
Publisher ‏ : ‎ CRC Press; 1st edition (August 23, 2024)
Publication date ‏ : ‎ August 23, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 34991 KB
Simultaneous device usage ‏ : ‎ Up to 4 simultaneous devices, per publisher limits
Text-to-Speech ‏ : ‎ Not enabled
Enhanced typesetting ‏ : ‎ Not Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Format ‏ : ‎ Print Replica


Explainable, Interpretable, and Transparent AI Systems: Why They Matter

In recent years, artificial intelligence (AI) has made significant advancements in various fields, from healthcare to finance to transportation. However, as AI systems become more complex and integrated into our daily lives, there is a growing need for these systems to be explainable, interpretable, and transparent.

Explainable AI refers to the ability of AI systems to provide clear explanations for their decisions and actions. This is crucial for building trust and understanding among users, especially in high-stakes applications such as healthcare and criminal justice. Interpretable AI goes a step further, allowing users to understand how the AI system arrived at its decisions and predictions. This can help identify biases, errors, and limitations in the system, as well as improve its overall performance.

Transparency in AI systems involves making the system’s inner workings and data sources accessible to users and stakeholders. This transparency is essential for accountability, compliance with regulations such as GDPR, and ensuring that AI systems are fair and ethical.

Overall, explainable, interpretable, and transparent AI systems are essential for building trust, ensuring accountability, and promoting ethical AI development. As AI continues to play a larger role in society, it is crucial that we prioritize these principles to create AI systems that benefit everyone.
#Explainable #Interpretable #Transparent #Systems

Per Anhalter durch die Galaxis des verantwortungsvollen maschinellen Lernens: The Hitchhiker’s Guide to Responsible Machine Learning (German Edition)

Leave a reply


Price: $10.91
(as of Dec 27,2024 01:47:13 UTC – Details)




Publisher ‏ : ‎ Scientific Foundation SmarterPoland.pl (February 2, 2023)
Language ‏ : ‎ German
Paperback ‏ : ‎ 52 pages
ISBN-10 ‏ : ‎ 8365291185
ISBN-13 ‏ : ‎ 978-8365291189
Item Weight ‏ : ‎ 7 ounces
Dimensions ‏ : ‎ 8.27 x 0.13 x 11.69 inches


Per Anhalter durch die Galaxis des verantwortungsvollen maschinellen Lernens: The Hitchhiker’s Guide to Responsible Machine Learning (German Edition)

Willkommen zur deutschen Ausgabe des ultimativen Leitfadens für verantwortungsvolles maschinelles Lernen! In dieser spannenden Reise durch die Galaxis des ML werden wir uns mit den ethischen und sozialen Implikationen von künstlicher Intelligenz, Algorithmen und Daten befassen.

Von der Bedeutung der Transparenz und Erklärbarkeit von Entscheidungen bis hin zur Vermeidung von Bias und Diskriminierung – dieses Buch bietet Ihnen einen umfassenden Überblick über die wichtigsten Themen im Bereich des verantwortungsvollen maschinellen Lernens.

Egal, ob Sie ein Einsteiger in die Welt des ML sind oder bereits Erfahrung in diesem Bereich haben, dieses Buch wird Ihnen helfen, die richtigen Fragen zu stellen und die richtigen Entscheidungen zu treffen, um sicherzustellen, dass Ihre ML-Modelle ethisch und sozial verantwortungsbewusst sind.

Begleiten Sie uns auf dieser aufregenden Reise und entdecken Sie, wie Sie die Macht des maschinellen Lernens nutzen können, um eine bessere und gerechtere Zukunft für alle zu schaffen. Machen Sie sich bereit, um Per Anhalter durch die Galaxis des verantwortungsvollen maschinellen Lernens zu reisen!
#Anhalter #durch #die #Galaxis #des #verantwortungsvollen #maschinellen #Lernens #Hitchhikers #Guide #Responsible #Machine #Learning #German #Edition

Learning Google Cloud Vertex AI: Build, deploy, and manage machine learning models with Vertex AI (English Edition)

Leave a reply


Price: $31.19
(as of Dec 27,2024 01:16:00 UTC – Details)




ASIN ‏ : ‎ B0CHYHYX2Y
Publisher ‏ : ‎ BPB Publications; 1st edition (August 28, 2023)
Publication date ‏ : ‎ August 28, 2023
Language ‏ : ‎ English
File size ‏ : ‎ 28839 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 494 pages


Are you interested in learning how to build, deploy, and manage machine learning models using Google Cloud Vertex AI? Look no further than the book “Learning Google Cloud Vertex AI: Build, deploy, and manage machine learning models with Vertex AI (English Edition)”.

This comprehensive guide covers everything you need to know about Vertex AI, Google’s machine learning platform that allows you to easily build and deploy models at scale. Whether you’re a beginner looking to get started with machine learning or an experienced data scientist looking to streamline your workflow, this book has something for everyone.

From understanding the basics of machine learning to advanced topics like model tuning and deployment, “Learning Google Cloud Vertex AI” has you covered. With practical examples and step-by-step tutorials, you’ll be able to quickly put your newfound knowledge into practice.

Don’t miss out on this opportunity to master Google Cloud Vertex AI and take your machine learning projects to the next level. Grab your copy of “Learning Google Cloud Vertex AI: Build, deploy, and manage machine learning models with Vertex AI (English Edition)” today!
#Learning #Google #Cloud #Vertex #Build #deploy #manage #machine #learning #models #Vertex #English #Edition

Responsible Data Science: Transparency and Fairness in Algorithms

Leave a reply


Price: $37.67
(as of Dec 27,2024 00:09:43 UTC – Details)




ASIN ‏ : ‎ B093B8Z4YK
Publisher ‏ : ‎ Wiley; 1st edition (April 21, 2021)
Publication date ‏ : ‎ April 21, 2021
Language ‏ : ‎ English
File size ‏ : ‎ 31005 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 282 pages


Data science has the power to transform industries and improve decision-making processes. However, with this power comes the responsibility to ensure transparency and fairness in the algorithms we create.

Transparency is essential in data science to build trust with stakeholders and ensure accountability. By documenting the data sources, methodologies, and assumptions used in developing algorithms, we can provide a clear understanding of how decisions are made. This transparency allows for greater scrutiny and ensures that biases and errors can be identified and addressed.

Fairness is another crucial aspect of responsible data science. Algorithms can inadvertently perpetuate bias and discrimination if not carefully designed and tested. By incorporating fairness metrics into our models and regularly monitoring for biases, we can ensure that decisions are made in an equitable manner.

As data scientists, it is our duty to prioritize transparency and fairness in our work. By doing so, we can build more reliable and ethical algorithms that benefit society as a whole. Let’s strive to create a future where data science is used responsibly and ethically for the betterment of all.
#Responsible #Data #Science #Transparency #Fairness #Algorithms

Convergence of Blockchain and Explainable Artificial Intelligence: BlockXAI (River Publishers Series in Digital Security and Forensics)

Leave a reply


Price: $4.99
(as of Dec 26,2024 23:36:08 UTC – Details)




ASIN ‏ : ‎ B0DGRKX6D9
Publisher ‏ : ‎ River Publishers; 1st edition (October 30, 2024)
Publication date ‏ : ‎ October 30, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 4999 KB
Simultaneous device usage ‏ : ‎ Up to 4 simultaneous devices, per publisher limits
Text-to-Speech ‏ : ‎ Not enabled
Enhanced typesetting ‏ : ‎ Not Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 160 pages
Format ‏ : ‎ Print Replica


The convergence of blockchain technology and explainable artificial intelligence (XAI) has the potential to revolutionize various industries and enhance transparency and accountability in decision-making processes. One of the latest publications in this field is “BlockXAI” from the River Publishers Series in Digital Security and Forensics.

This groundbreaking book delves into the intersection of blockchain and XAI, exploring how these technologies can work together to create more trustworthy and interpretable AI systems. The authors discuss the challenges and opportunities of combining blockchain’s decentralized and immutable ledger with XAI’s ability to provide insights into AI decision-making processes.

Through case studies and real-world examples, “BlockXAI” demonstrates how this convergence can be leveraged in areas such as finance, healthcare, supply chain management, and more. By integrating blockchain and XAI, organizations can ensure the fairness, accountability, and transparency of AI systems, ultimately building trust with stakeholders and users.

Whether you are a researcher, practitioner, or industry professional interested in the intersection of blockchain and XAI, “BlockXAI” offers valuable insights and practical guidance for leveraging these technologies in your own projects. Stay ahead of the curve and discover the potential of blockchain and XAI with this essential read from the River Publishers Series in Digital Security and Forensics.
#Convergence #Blockchain #Explainable #Artificial #Intelligence #BlockXAI #River #Publishers #Series #Digital #Security #Forensics

Explainable and Transparent AI and Multi-Agent Systems: 5th International Worksh

Leave a reply



Explainable and Transparent AI and Multi-Agent Systems: 5th International Worksh

Price : 51.03

Ends on : N/A

View on eBay
op on XAI-MAS

In today’s rapidly evolving technological landscape, the use of artificial intelligence (AI) and multi-agent systems (MAS) has become increasingly prevalent. However, concerns around the opacity and lack of transparency in these systems have led to the development of Explainable AI (XAI) and Transparent AI frameworks. These frameworks aim to ensure that AI and MAS systems are not only effective in their decision-making processes but also understandable and accountable.

The 5th International Workshop on XAI-MAS will bring together experts from various disciplines to discuss the latest research and advancements in the field of explainable and transparent AI and MAS. Participants will have the opportunity to explore topics such as interpretability, fairness, accountability, and trust in AI and MAS systems, as well as discuss practical applications and case studies.

Join us at the 5th International Workshop on XAI-MAS to gain insights into how we can create AI and MAS systems that are not only efficient and effective but also transparent and explainable. Let’s work together to ensure that these technologies are developed and deployed in a responsible and ethical manner.
#Explainable #Transparent #MultiAgent #Systems #5th #International #Worksh

Python explainable AI (XAI) combat(Chinese Edition)

Leave a reply


Price: $50.50
(as of Dec 26,2024 23:03:01 UTC – Details)




Publisher ‏ : ‎ Tsinghua University Press (August 1, 2022)
Language ‏ : ‎ Chinese
ISBN-10 ‏ : ‎ 730261329X
ISBN-13 ‏ : ‎ 978-7302613299


Python解释性人工智能（XAI）对抗（中文版）

在这个帖子中，我们将探讨Python解释性人工智能（XAI）在对抗中的应用。解释性人工智能是一种能够解释其决策过程的人工智能系统，与黑盒模型相比，它更易于理解和解释。

Python是一种流行的编程语言，被广泛用于开发人工智能系统。结合Python和解释性人工智能，我们可以构建具有可解释性和透明性的AI系统。

在对抗中，Python解释性人工智能可以帮助我们理解机器学习模型的决策过程，从而更好地预测和应对对手的行为。通过分析模型的特征重要性和决策路径，我们可以更好地了解AI系统的工作原理，从而更好地应对不同情况。

总的来说，Python解释性人工智能在对抗中的应用可以帮助我们更好地理解和控制AI系统的决策过程，从而提高我们在竞争中的优势。如果您对这个话题感兴趣，欢迎加入我们的讨论！
#Python #explainable #XAI #combatChinese #Edition

Explainable Ai With Python, Paperback by Gianfagna, Leonida; Di Cecco, Antoni…

Leave a reply



Explainable Ai With Python, Paperback by Gianfagna, Leonida; Di Cecco, Antoni…

Price : 73.64

Ends on : N/A

View on eBay
In this post, we will explore the book “Explainable AI with Python” written by Gianfagna, Leonida and Di Cecco, Antoni.

This comprehensive guide covers the fundamentals of Explainable AI (XAI) and how it can be implemented using Python. The book provides a step-by-step approach to developing interpretable AI models, allowing users to understand the reasoning behind the decisions made by machine learning algorithms.

Topics covered in the book include techniques such as LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and feature importance analysis. Readers will learn how to visualize and interpret model predictions, making AI more transparent and trustworthy.

Whether you are a data scientist, machine learning engineer, or AI enthusiast, “Explainable AI with Python” is a valuable resource for anyone looking to delve deeper into the world of interpretable AI. Get your hands on a copy today and unlock the power of explainability in AI.
#Explainable #Python #Paperback #Gianfagna #Leonida #Cecco #Antoni..

Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems (Lecture Notes in Artificial Intelligence)

Leave a reply


Price: $54.99
(as of Dec 26,2024 21:56:41 UTC – Details)




Publisher ‏ : ‎ Springer; 1st ed. 2019 edition (January 4, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 188 pages
ISBN-10 ‏ : ‎ 3030374459
ISBN-13 ‏ : ‎ 978-3030374457
Item Weight ‏ : ‎ 9.6 ounces
Dimensions ‏ : ‎ 6.1 x 0.43 x 9.25 inches


Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems (Lecture Notes in Artificial Intelligence)

Artificial Intelligence (AI) has the potential to revolutionize the field of medicine by improving diagnosis, treatment, and patient care. However, one of the key challenges in deploying AI in healthcare is ensuring that the systems are transparent and explainable. This is crucial for gaining trust from both healthcare providers and patients.

In this lecture notes series on AI in Medicine, we delve into the importance of knowledge representation and building transparent and explainable AI systems. Knowledge representation is essential for capturing and organizing medical knowledge in a way that AI systems can effectively utilize. By encoding medical knowledge in a structured format, AI algorithms can make more accurate and informed decisions.

Additionally, transparency and explainability are critical for ensuring that AI systems are trustworthy and can be easily understood by healthcare professionals. Transparent AI systems provide insights into how they arrive at their decisions, allowing doctors to validate and interpret the results. Explainable AI systems can also help patients understand the reasoning behind their diagnosis and treatment plans, leading to increased confidence in the technology.

Overall, this lecture notes series will explore the intersection of AI, knowledge representation, and transparency in the field of medicine. By addressing these key aspects, we can pave the way for the widespread adoption of AI in healthcare, ultimately improving patient outcomes and revolutionizing the way medical professionals deliver care.
#Artificial #Intelligence #Medicine #Knowledge #Representation #Transparent #Explainable #Systems #Lecture #Notes #Artificial #Intelligence

Explainable AI Applications for Human Behavior Analysis by P. Paramasivan Paperb

Leave a reply



Explainable AI Applications for Human Behavior Analysis by P. Paramasivan Paperb

Price : 310.17

Ends on : N/A

View on eBay
In the paper titled “Explainable AI Applications for Human Behavior Analysis” by P. Paramasivan, the author explores the use of explainable artificial intelligence (AI) in analyzing human behavior.

The paper discusses how AI technologies can be used to analyze and understand human behavior in various contexts, such as healthcare, education, and social media. By using explainable AI, researchers and practitioners can gain insights into why individuals behave in certain ways, and how these behaviors can be influenced or changed.

The author highlights the importance of transparency and interpretability in AI models, especially when dealing with sensitive topics such as human behavior. By providing explanations for the AI’s decisions and recommendations, users can better understand and trust the technology.

Overall, the paper emphasizes the potential of explainable AI in improving our understanding of human behavior and guiding interventions to promote positive changes. It serves as a valuable resource for researchers, practitioners, and policymakers interested in leveraging AI for behavior analysis.
#Explainable #Applications #Human #Behavior #Analysis #Paramasivan #Paperb