This file aims to give a thorough overview of the current state and future prospects of interpretability and explainability in deep learning, making it a valuable resource for students, researchers, and professionals in the field. The post will comprehensively cover the following aspects:
Introduction to Interpretability and Explainability: Explaining what these concepts mean in the context of deep learning and why they are critical.
The Need for Transparency: Discussing the importance of interpretability and explainability in AI, focusing on ethical considerations, trust in AI systems, and regulatory compliance.
Key Concepts and Definitions: Clarifying terms like “black-box” models, interpretability, explainability, and their relevance in deep learning.
Methods and Techniques:
Visualization Techniques: Detailing methods like feature visualization, attention mechanisms, and tools like Grad-CAM.
Feature Importance Analysis: Exploring techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) for understanding feature contributions.
Decision Boundary Analysis: Discussing methods to analyze and visualize the decision boundaries of models.
Practical Implementations and Code Examples: Providing examples of how these techniques can be implemented using popular deep learning frameworks like TensorFlow or PyTorch.
Case Studies and Real-World Applications: Presenting real-world scenarios where interpretability and explainability have played a vital role, especially in fields like healthcare, finance, and autonomous systems.
Challenges and Limitations: Addressing the challenges in achieving interpretability and the trade-offs with model complexity and performance.
Future Directions and Research Trends: Discussing ongoing research, emerging trends, and potential future advancements in making deep learning models more interpretable and explainable.
Conclusion: Summarizing the key takeaways and the importance of continued efforts in this area.
References and Further Reading: Providing a list of academic papers, articles, and resources for readers who wish to delve deeper into the topic.
Section 1: Introduction to Interpretability and Explainability
The field of deep learning has witnessed exponential growth in recent years, leading to significant advancements in various applications such as image recognition, natural language processing, and autonomous systems. However, as these neural network models become increasingly complex, they often resemble “black boxes”, where the decision-making process is not transparent or understandable to users. This obscurity raises concerns, especially in critical applications, and underscores the need for interpretability and explainability in deep learning models.
What are Interpretability and Explainability?
Interpretability: This refers to the degree to which a human can understand the cause of a decision made by a machine learning model. It’s about answering the questio
DALL-E 2 - OpenAI imagery automation first developed by Vishal Coodye in 2021...MITAILibrary
Vishal Coodye is an MIT fellow who has contributed to the Robotics & AI Technology since 2010. His contributions to the scientific comminity brings the world to new horizons. MIT. Library. USA.
Interpretable Machine Learning_ Techniques for Model Explainability.Tyrion Lannister
In this article, we will explore the importance of interpretable machine learning, its techniques, and its significance in the ever-evolving field of artificial intelligence.
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneJames Anderson
If Artificial Intelligence (AI) is a black-box, how can a human comprehend and trust the results of Machine Learning (ML) alogrithms? Explainable AI (XAI) tries to shed light into that AI black-box so humans can trust what is going on. Our speaker Meg Dickey-Kurdziolek is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. Recording of the presentation: https://youtu.be/6N2DNN_HDWU
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
Explainable AI (XAI) stands to address all these challenges and focuses on developing methods and techniques that bring transparency and comprehensibility to AI systems. Its primary objective is to empower users with a clear understanding of the reasoning and logic behind AI algorithms’ decisions.
"I don't trust AI": the role of explainability in responsible AIErika Agostinelli
This Tech talk was part of the Women in Data Science 2021 Bristol Event. An Introduction to Explainable AI: what to consider when developing an explainable strategy, what are the state of the art techniques and open-source tools used in the field, with concrete examples. To see the recording: https://www.crowdcast.io/e/o4gjxatp
DALL-E 2 - OpenAI imagery automation first developed by Vishal Coodye in 2021...MITAILibrary
Vishal Coodye is an MIT fellow who has contributed to the Robotics & AI Technology since 2010. His contributions to the scientific comminity brings the world to new horizons. MIT. Library. USA.
Interpretable Machine Learning_ Techniques for Model Explainability.Tyrion Lannister
In this article, we will explore the importance of interpretable machine learning, its techniques, and its significance in the ever-evolving field of artificial intelligence.
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneJames Anderson
If Artificial Intelligence (AI) is a black-box, how can a human comprehend and trust the results of Machine Learning (ML) alogrithms? Explainable AI (XAI) tries to shed light into that AI black-box so humans can trust what is going on. Our speaker Meg Dickey-Kurdziolek is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. Recording of the presentation: https://youtu.be/6N2DNN_HDWU
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
Explainable AI (XAI) stands to address all these challenges and focuses on developing methods and techniques that bring transparency and comprehensibility to AI systems. Its primary objective is to empower users with a clear understanding of the reasoning and logic behind AI algorithms’ decisions.
"I don't trust AI": the role of explainability in responsible AIErika Agostinelli
This Tech talk was part of the Women in Data Science 2021 Bristol Event. An Introduction to Explainable AI: what to consider when developing an explainable strategy, what are the state of the art techniques and open-source tools used in the field, with concrete examples. To see the recording: https://www.crowdcast.io/e/o4gjxatp
An Explanation Framework for Interpretable Credit Scoring gerogepatton
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech),
applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This
deficiency of transparency limits their application in different domains including credit scoring. Credit
scoring systems help financial experts make better decisions regarding whether or not to accept a loan
application so that loans with a high probability of default are not accepted. Apart from the noisy and
highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the
`right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit
Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic
decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which
focuses on making black-box models more interpretable. In this work, we present a credit scoring model
that is both accurate and interpretable. For classification, state-of-the-art performance on the Home
Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient
Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework,
which provides different explanations (i.e. global, local feature-based and local instance- based) that are
required by different people in different situations. Evaluation through the use of functionally-grounded,
application-grounded and human-grounded analysis shows that the explanations provided are simple and
consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.
AN EXPLANATION FRAMEWORK FOR INTERPRETABLE CREDIT SCORINGijaia
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech),
applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This
deficiency of transparency limits their application in different domains including credit scoring. Credit
scoring systems help financial experts make better decisions regarding whether or not to accept a loan
application so that loans with a high probability of default are not accepted. Apart from the noisy and
highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the
`right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit
Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic
decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which
focuses on making black-box models more interpretable. In this work, we present a credit scoring model
that is both accurate and interpretable. For classification, state-of-the-art performance on the Home
Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient
Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework,
which provides different explanations (i.e. global, local feature-based and local instance- based) that are
required by different people in different situations. Evaluation through the use of functionally-grounded,
application-grounded and human-grounded analysis shows that the explanations provided are simple and
consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.
Keynote by Charles Elkan, Goldman Sachs - Machine Learning in Finance - The P...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/7h61dxKjhvg
Machine learning in finance—the promise and the peril
This talk will discuss how machine learning (ML) fits into the landscape of quantitative methods used in finance, and draw conclusions about application domains where ML is more promising versus domains where the perils are more acute. The talk will also discuss how to formulate a financial goal as an ML problem, and how to choose between solution approaches.
Bio: Leading projects to apply machine learning and artificial intelligence across the firm. Evaluating opportunities to work with other organizations and consulting with clients.
Oleksander Krakovetskyi "Explaining a Machine Learning blackbox"Fwdays
As Data Scientists we want to understand machine learning models we have built. “Why did my model make this mistake?”, “Does my model discriminate?”, “How can I understand and trust the model's decisions?”, “Does my model satisfy legal requirements?” are commonly asked questions.
In this presentation we will talk about machine learning explainability and interpretability - two concepts that could help us really understand ML models.
Website: https://fwdays.com/en/event/data-science-fwdays-2019/review/explaining-a-machine-learning-blackbox
This follow up post on the subject of Artificial Intelligence focuses on Expert Systems and the role of traditional experts in their design and development. It explores four main themes:
What do we mean by Expert?
How do experts work?
Expert Systems Application Domains, and
Features of rule based Expert (KB) Systems
invited talk in the ExUM workshop in the UMAP 2022 conference
abstract:
Explainability has become an important topic both in Data Science and AI in general and in recommender systems in particular, as algorithms have become much less inherently explainable. However, explainability has different interpretations and goals in different fields. For example, interpretability and explanainability tools in machine learning are predominantly developed for Data Scientists to understand and scrutinize their models. Current tools are therefore often quite technical and not very ‘user-friendly’. I will illustrate this with our recent work on improving the explainability of model-agnostic tools such as LIME and SHAP. Another stream of research on explainability in the HCI and XAI fields focuses more on users’ needs for explainability, such as contrastive and selective explanations and explanations that fit with the mental models and beliefs of the user. However, how to satisfy those needs is still an open question. Based on recent work in interactive AI and machine learning, I will propose that explainability goes together with interactivity, and will illustrate this with examples from our own work in music genre exploration, that combines visualizations and interactive tools to help users understand and tune our exploration model.
Discussion - Weeks 1–2COLLAPSETop of FormShared Practice—Rol.docxcuddietheresa
Discussion - Weeks 1–2
COLLAPSE
Top of Form
Shared Practice—Role of Business Information Systems
Note: This Discussion has slightly different due dates than what is typical for this program. Be mindful of this as you post and respond in the Discussion. Your post is due on Day 7 and your Response is due on Day 3 of Week 2.
As a manager, it is critical for you to understand the types of business information systems available to support business operations, management, and strategy. As of 2013, these include, but are certainly not limited to the following:
· Supply Chain Management (SCM)
· Accounting Information System
· Customer Relationship Management (CRM)
· Decision Support Systems (DSS)
· Enterprise Resource Planning (ERP)
· Human Resource Management
These types of systems support critical business functions and operations that every organization must manage. The effective manager understands the purpose of these types of systems and how they can be best used to manage the organization's data and information.
In this Discussion, you will share your knowledge and findings related to business information systems and the role they play in your organization. You will also consider your colleagues' experiences to explore additional ways business information systems might be applied in your colleagues' organizations, or an organization with which you are familiar.
By Day 7
· Describe two or three of the more important technologies or business information systems used in your organization, or in one with which you are familiar.
· Discuss two examples of how these business information systems are affecting the organization you selected. Be sure to discuss how individual behaviors and organizational or individual processes are changing and what you can learn from the issues encountered.
· Summarize what you have learned about the importance of business information systems and why managers need to understand how systems can be used to the organization's advantage.
You should find and use at least one additional current article from a credible resource, either from the Walden Library or the Internet. Please be specific, and remember to use citations and references as necessary.
General Guidance: Your initial Discussion post, due by Day 7, will typically be 3–4 paragraphs in length as a general expectation/estimate. Refer to the rubric for the Week 1 Discussion for grading elements and criteria. Your Instructor will use the rubric to assess your work.
Week 2
By Day 3
In your Week 1 Discussion you described how business information systems have been applied in an organization with which you are familiar. Read through your colleagues' posts and by Day 3 (Week 2), respond to two of your colleagues in one or more of the following ways:
· Examine how the business information systems described by your colleague could be or are being used by your organization. Offer additional ways either organization might take advantage of these systems.
· Examine how the b ...
Sharda_dss11_im_01.docChapter 1An Overview of Analy.docxklinda1
Sharda_dss11_im_01.doc
Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
· Understand the need for computerized support of managerial decision making
· Understand the development of systems for providing decision-making support
· Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence
· Describe the business intelligence (BI) methodology and concepts
· Understand the different types of analytics and review selected applications
· Understand the basic concepts of artificial intelligence (AI) and see selected applications
· Understand the analytics ecosystem to identify various key players and career opportunities
CHAPTER OVERVIEW
The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized support portfolio for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support as well as on the commercial tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications. However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata.
Sharda_dss11_im_01.docChapter 1An Overview of Analy.docxlesleyryder69361
Sharda_dss11_im_01.doc
Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
· Understand the need for computerized support of managerial decision making
· Understand the development of systems for providing decision-making support
· Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence
· Describe the business intelligence (BI) methodology and concepts
· Understand the different types of analytics and review selected applications
· Understand the basic concepts of artificial intelligence (AI) and see selected applications
· Understand the analytics ecosystem to identify various key players and career opportunities
CHAPTER OVERVIEW
The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized support portfolio for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support as well as on the commercial tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications. However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata.
Unveiling the Power of Machine Learning.docxgreendigital
Introduction:
In the vast landscape of technological evolution, Machine Learning (ML) stands as a beacon of innovation. Reshaping the way we interact with the digital world. With its roots in artificial intelligence. ML empowers systems to learn and improve from experience without explicit programming. This transformative technology is at the forefront of revolutionizing industries, from healthcare to finance. and from manufacturing to entertainment. In this article, we delve into the intricacies of machine learning. exploring its applications, challenges, and the profound impact it has on shaping the future.
Rsqrd AI - Challenges in Deploying Explainable Machine LearningAlessya Visnjic
In this talk, Umang Bhatt presents his work on understanding how explainability is used in the industry, research done in collaboration with Partnership on AI. Umang Bhatt is a Research Fellow at the Partnership on AI and a Ph.D. student in the Machine Learning Group at the University of Cambridge. His research interests lie in statistical machine learning, explainable artificial intelligence, and human-machine collaboration.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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An Explanation Framework for Interpretable Credit Scoring gerogepatton
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech),
applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This
deficiency of transparency limits their application in different domains including credit scoring. Credit
scoring systems help financial experts make better decisions regarding whether or not to accept a loan
application so that loans with a high probability of default are not accepted. Apart from the noisy and
highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the
`right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit
Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic
decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which
focuses on making black-box models more interpretable. In this work, we present a credit scoring model
that is both accurate and interpretable. For classification, state-of-the-art performance on the Home
Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient
Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework,
which provides different explanations (i.e. global, local feature-based and local instance- based) that are
required by different people in different situations. Evaluation through the use of functionally-grounded,
application-grounded and human-grounded analysis shows that the explanations provided are simple and
consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.
AN EXPLANATION FRAMEWORK FOR INTERPRETABLE CREDIT SCORINGijaia
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech),
applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This
deficiency of transparency limits their application in different domains including credit scoring. Credit
scoring systems help financial experts make better decisions regarding whether or not to accept a loan
application so that loans with a high probability of default are not accepted. Apart from the noisy and
highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the
`right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit
Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic
decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which
focuses on making black-box models more interpretable. In this work, we present a credit scoring model
that is both accurate and interpretable. For classification, state-of-the-art performance on the Home
Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient
Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework,
which provides different explanations (i.e. global, local feature-based and local instance- based) that are
required by different people in different situations. Evaluation through the use of functionally-grounded,
application-grounded and human-grounded analysis shows that the explanations provided are simple and
consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.
Keynote by Charles Elkan, Goldman Sachs - Machine Learning in Finance - The P...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/7h61dxKjhvg
Machine learning in finance—the promise and the peril
This talk will discuss how machine learning (ML) fits into the landscape of quantitative methods used in finance, and draw conclusions about application domains where ML is more promising versus domains where the perils are more acute. The talk will also discuss how to formulate a financial goal as an ML problem, and how to choose between solution approaches.
Bio: Leading projects to apply machine learning and artificial intelligence across the firm. Evaluating opportunities to work with other organizations and consulting with clients.
Oleksander Krakovetskyi "Explaining a Machine Learning blackbox"Fwdays
As Data Scientists we want to understand machine learning models we have built. “Why did my model make this mistake?”, “Does my model discriminate?”, “How can I understand and trust the model's decisions?”, “Does my model satisfy legal requirements?” are commonly asked questions.
In this presentation we will talk about machine learning explainability and interpretability - two concepts that could help us really understand ML models.
Website: https://fwdays.com/en/event/data-science-fwdays-2019/review/explaining-a-machine-learning-blackbox
This follow up post on the subject of Artificial Intelligence focuses on Expert Systems and the role of traditional experts in their design and development. It explores four main themes:
What do we mean by Expert?
How do experts work?
Expert Systems Application Domains, and
Features of rule based Expert (KB) Systems
invited talk in the ExUM workshop in the UMAP 2022 conference
abstract:
Explainability has become an important topic both in Data Science and AI in general and in recommender systems in particular, as algorithms have become much less inherently explainable. However, explainability has different interpretations and goals in different fields. For example, interpretability and explanainability tools in machine learning are predominantly developed for Data Scientists to understand and scrutinize their models. Current tools are therefore often quite technical and not very ‘user-friendly’. I will illustrate this with our recent work on improving the explainability of model-agnostic tools such as LIME and SHAP. Another stream of research on explainability in the HCI and XAI fields focuses more on users’ needs for explainability, such as contrastive and selective explanations and explanations that fit with the mental models and beliefs of the user. However, how to satisfy those needs is still an open question. Based on recent work in interactive AI and machine learning, I will propose that explainability goes together with interactivity, and will illustrate this with examples from our own work in music genre exploration, that combines visualizations and interactive tools to help users understand and tune our exploration model.
Discussion - Weeks 1–2COLLAPSETop of FormShared Practice—Rol.docxcuddietheresa
Discussion - Weeks 1–2
COLLAPSE
Top of Form
Shared Practice—Role of Business Information Systems
Note: This Discussion has slightly different due dates than what is typical for this program. Be mindful of this as you post and respond in the Discussion. Your post is due on Day 7 and your Response is due on Day 3 of Week 2.
As a manager, it is critical for you to understand the types of business information systems available to support business operations, management, and strategy. As of 2013, these include, but are certainly not limited to the following:
· Supply Chain Management (SCM)
· Accounting Information System
· Customer Relationship Management (CRM)
· Decision Support Systems (DSS)
· Enterprise Resource Planning (ERP)
· Human Resource Management
These types of systems support critical business functions and operations that every organization must manage. The effective manager understands the purpose of these types of systems and how they can be best used to manage the organization's data and information.
In this Discussion, you will share your knowledge and findings related to business information systems and the role they play in your organization. You will also consider your colleagues' experiences to explore additional ways business information systems might be applied in your colleagues' organizations, or an organization with which you are familiar.
By Day 7
· Describe two or three of the more important technologies or business information systems used in your organization, or in one with which you are familiar.
· Discuss two examples of how these business information systems are affecting the organization you selected. Be sure to discuss how individual behaviors and organizational or individual processes are changing and what you can learn from the issues encountered.
· Summarize what you have learned about the importance of business information systems and why managers need to understand how systems can be used to the organization's advantage.
You should find and use at least one additional current article from a credible resource, either from the Walden Library or the Internet. Please be specific, and remember to use citations and references as necessary.
General Guidance: Your initial Discussion post, due by Day 7, will typically be 3–4 paragraphs in length as a general expectation/estimate. Refer to the rubric for the Week 1 Discussion for grading elements and criteria. Your Instructor will use the rubric to assess your work.
Week 2
By Day 3
In your Week 1 Discussion you described how business information systems have been applied in an organization with which you are familiar. Read through your colleagues' posts and by Day 3 (Week 2), respond to two of your colleagues in one or more of the following ways:
· Examine how the business information systems described by your colleague could be or are being used by your organization. Offer additional ways either organization might take advantage of these systems.
· Examine how the b ...
Sharda_dss11_im_01.docChapter 1An Overview of Analy.docxklinda1
Sharda_dss11_im_01.doc
Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
· Understand the need for computerized support of managerial decision making
· Understand the development of systems for providing decision-making support
· Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence
· Describe the business intelligence (BI) methodology and concepts
· Understand the different types of analytics and review selected applications
· Understand the basic concepts of artificial intelligence (AI) and see selected applications
· Understand the analytics ecosystem to identify various key players and career opportunities
CHAPTER OVERVIEW
The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized support portfolio for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support as well as on the commercial tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications. However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata.
Sharda_dss11_im_01.docChapter 1An Overview of Analy.docxlesleyryder69361
Sharda_dss11_im_01.doc
Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
· Understand the need for computerized support of managerial decision making
· Understand the development of systems for providing decision-making support
· Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence
· Describe the business intelligence (BI) methodology and concepts
· Understand the different types of analytics and review selected applications
· Understand the basic concepts of artificial intelligence (AI) and see selected applications
· Understand the analytics ecosystem to identify various key players and career opportunities
CHAPTER OVERVIEW
The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized support portfolio for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support as well as on the commercial tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications. However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata.
Unveiling the Power of Machine Learning.docxgreendigital
Introduction:
In the vast landscape of technological evolution, Machine Learning (ML) stands as a beacon of innovation. Reshaping the way we interact with the digital world. With its roots in artificial intelligence. ML empowers systems to learn and improve from experience without explicit programming. This transformative technology is at the forefront of revolutionizing industries, from healthcare to finance. and from manufacturing to entertainment. In this article, we delve into the intricacies of machine learning. exploring its applications, challenges, and the profound impact it has on shaping the future.
Rsqrd AI - Challenges in Deploying Explainable Machine LearningAlessya Visnjic
In this talk, Umang Bhatt presents his work on understanding how explainability is used in the industry, research done in collaboration with Partnership on AI. Umang Bhatt is a Research Fellow at the Partnership on AI and a Ph.D. student in the Machine Learning Group at the University of Cambridge. His research interests lie in statistical machine learning, explainable artificial intelligence, and human-machine collaboration.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
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The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
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Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
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Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
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A Survey of Techniques for Maximizing LLM Performance.pptx
Improved Interpretability and Explainability of Deep Learning Models.pdf
1. Improved Interpretability and Explainability of Deep
Learning Models
This file aims to give a thorough overview of the current state and future prospects of
interpretability and explainability in deep learning, making it a valuable resource for students,
researchers, and professionals in the field. The post will comprehensively cover the following
aspects:
● Introduction to Interpretability and Explainability: Explaining what these concepts
mean in the context of deep learning and why they are critical.
● The Need for Transparency: Discussing the importance of interpretability and
explainability in AI, focusing on ethical considerations, trust in AI systems, and regulatory
compliance.
● Key Concepts and Definitions: Clarifying terms like “black-box” models, interpretability,
explainability, and their relevance in deep learning.
● Methods and Techniques:
○ Visualization Techniques: Detailing methods like feature visualization, attention
mechanisms, and tools like Grad-CAM.
○ Feature Importance Analysis: Exploring techniques like SHAP (SHapley
Additive exPlanations) and LIME (Local Interpretable Model-agnostic
Explanations) for understanding feature contributions.
○ Decision Boundary Analysis: Discussing methods to analyze and visualize the
decision boundaries of models.
● Practical Implementations and Code Examples: Providing examples of how these
techniques can be implemented using popular deep learning frameworks like
TensorFlow or PyTorch.
● Case Studies and Real-World Applications: Presenting real-world scenarios where
interpretability and explainability have played a vital role, especially in fields like
healthcare, finance, and autonomous systems.
● Challenges and Limitations: Addressing the challenges in achieving interpretability and
the trade-offs with model complexity and performance.
● Future Directions and Research Trends: Discussing ongoing research, emerging
trends, and potential future advancements in making deep learning models more
interpretable and explainable.
● Conclusion: Summarizing the key takeaways and the importance of continued efforts in
this area.
● References and Further Reading: Providing a list of academic papers, articles, and
resources for readers who wish to delve deeper into the topic.
2. Section 1: Introduction to Interpretability and
Explainability
The field of deep learning has witnessed exponential growth in recent years, leading to
significant advancements in various applications such as image recognition, natural language
processing, and autonomous systems. However, as these neural network models become
increasingly complex, they often resemble “black boxes”, where the decision-making process is
not transparent or understandable to users. This obscurity raises concerns, especially in critical
applications, and underscores the need for interpretability and explainability in deep learning
models.
What are Interpretability and Explainability?
● Interpretability: This refers to the degree to which a human can understand the cause
of a decision made by a machine learning model. It’s about answering the question,
“Why did the model make this prediction?” Interpretability is crucial in validating the
model’s behavior and ensuring it aligns with real-world expectations.
● Explainability: Closely related to interpretability, explainability involves the ability to
explain both the processes and results of the model in human terms. It’s about
conveying an understanding of the model’s mechanisms in a comprehensible way.
Why are They Important?
● Trust and Reliability: For users and stakeholders to trust AI-driven decisions, especially
in high-stakes domains like healthcare or finance, it’s essential they understand how
these decisions are made.
● Ethical AI Practices: Understanding model decisions is critical for identifying and
mitigating biases, ensuring fair and ethical AI practices.
● Regulatory Compliance: With regulations like the EU’s General Data Protection
Regulation (GDPR), there’s increasing legal emphasis on the transparency of AI
systems, particularly in terms of how personal data is used in decision-making.
The “Black Box” Challenge
Deep learning models, especially those with complex architectures like deep neural networks,
often operate as “black boxes.” While they can achieve high accuracy, the intricacies of their
internal decision paths are not easily decipherable. This lack of transparency can be
problematic in scenarios where understanding the rationale behind a decision is as important as
the decision itself.
Bridging the Gap
The goal of improved interpretability and explainability is to bridge the gap between AI
performance and human understanding. This involves developing methodologies and tools that
3. can shed light on the internal workings of complex models, thereby making AI more transparent
and accountable.
Section 2: The Importance of Transparency in AI
The Imperative of Understanding AI Decisions
In this section, we delve into the significance of transparency in AI systems, especially those
powered by deep learning. The increasing deployment of AI in various sectors necessitates a
clear understanding of how these systems make decisions, and more importantly, why these
decisions are made.
Trust and Credibility in AI Systems
● Building Trust: For users to rely on and accept AI-driven decisions, particularly in
high-stakes areas like healthcare, law enforcement, or financial services, there must be
a foundational level of trust. This trust is primarily built through transparency and the
ability to understand and verify AI decisions.
● Credibility and Reliability: The credibility of an AI system is closely tied to its
transparency. A system that can explain its decisions is more likely to be perceived as
reliable and credible.
Ethical and Fair AI Practices
● Detecting and Correcting Biases: AI systems can inadvertently learn and perpetuate
biases present in their training data. Transparency in AI helps in identifying such biases
and ensuring decisions are fair and ethical.
● Ensuring Accountability: When AI systems make decisions that affect people’s lives,
it’s crucial to have accountability mechanisms in place. Transparency facilitates
accountability by making it possible to trace and understand the decision-making
process.
Regulatory and Legal Compliance
● Adhering to Regulations: With the growing focus on data privacy and ethical AI,
regulations like the GDPR in Europe emphasize the need for explainable AI. Compliance
with such regulations is not only a legal requirement but also an ethical responsibility.
● Legal Justification of Decisions: In some scenarios, especially in legal or financial
contexts, AI decisions may need to be justified in court or to regulatory bodies.
Transparency and explainability enable this justification.
Section 3: Key Concepts and Definitions in AI
Interpretability and Explainability
4. Delineating Core Concepts
This section provides a deeper understanding of the fundamental concepts underpinning
interpretability and explainability in AI. It clarifies essential terms and their significance in the
context of deep learning.
1. Interpretability: This concept pertains to the extent to which a human can comprehend
and consistently predict a model’s outcome. Interpretability is often categorized into two
types:
○ Intrinsic Interpretability: This is inherent in simpler models where the
decision-making process is readily understandable (e.g., decision trees).
○ Post-hoc Interpretability: This applies to complex models (like deep neural
networks) and involves techniques used after model training to explain its
decisions.
2. Explainability: While closely related to interpretability, explainability goes a step further.
It’s not just about a model’s decisions being understandable, but also about being able to
explain them in human terms. This involves conveying the model’s functionality and
decision-making process in a way that humans can grasp.
3. Transparency: Often used interchangeably with interpretability and explainability,
transparency in AI refers to the clarity and openness with which a model’s mechanisms
and decisions can be understood by humans.
4. The Black Box Problem: This term describes the situation where the internal workings
of a model (especially in complex neural networks) are not visible or understandable.
The challenge is to open this ‘black box’ to make AI decisions more transparent and
accountable.
Importance of These Concepts
● These concepts are crucial for establishing trust, ethical compliance, and practical
applicability of AI in sensitive and impactful domains.
● Understanding these terms is the first step in addressing the challenges posed by
complex AI models in terms of their interpretability and accountability.
Section 4: Methods and Techniques for AI Interpretability
and Explainability
Overview
In this section, we delve into various methods and techniques employed to enhance the
interpretability and explainability of deep learning models. These methodologies provide insights
into how AI models make decisions, thereby making these processes more transparent.
Visualization Techniques
5. 1. Feature Visualization:
○ Purpose: Helps in understanding what features a model is focusing on.
○ Techniques: Includes creating activation maps and saliency maps.
○ Applications: Useful in models where visual input plays a key role, like image
classification.
○ Reference: “Visualizing and Understanding Convolutional Networks” by Zeiler
and Fergus provides foundational insights into feature visualization in CNNs.
2. Grad-CAM:
○ Purpose: Provides insights into which regions of the input image are important
for predictions.
○ Technique: Uses gradients flowing into the final convolutional layer for
localization.
○ Applications: Widely used in image recognition tasks for understanding model
focus areas.
○ Reference: The original Grad-CAM paper by Ramprasaath R. Selvaraju et al.
offers a comprehensive understanding of this method.
Feature Importance Analysis
1. SHAP (SHapley Additive exPlanations):
○ Purpose: To interpret the impact of having certain values for predictor variables.
○ Technique: SHAP values are calculated to show the contribution of each feature
to the prediction.
○ Applications: Useful in complex models for both global and local explanations.
○ Reference: “A Unified Approach to Interpreting Model Predictions” by Scott
Lundberg and Su-In Lee provides a detailed discussion on SHAP.
2. LIME (Local Interpretable Model-agnostic Explanations):
○ Purpose: To explain individual predictions regardless of the classifier used.
○ Technique: Approximates complex models locally with an interpretable model.
○ Applications: Can be used across various types of models for local
explanations.
○ Reference: The foundational paper on LIME by Marco Tulio Ribeiro et al.
outlines the methodology in detail.
Decision Boundary Analysis
1. Decision Trees as Surrogate Models:
○ Purpose: To approximate complex model decision boundaries with simpler
models.
○ Technique: A decision tree is trained to mimic the predictions of a complex
model.
○ Applications: Useful for explaining complex models in a more understandable
format.
○ Reference: “Interpretable Machine Learning” by Christoph Molnar discusses
surrogate models as a means of interpretability.
6. 2. Sensitivity Analysis:
○ Purpose: To understand how slight changes in input affect the model’s output.
○ Technique: Involves perturbing inputs and observing the variation in outputs.
○ Applications: Important in models where input features are closely interrelated.
○ Reference: “Sensitivity Analysis in Neural Networks” by Saltelli and Annoni
provides insights into this approach.
Section 5: Practical Implementations and Code Examples
Demonstrating Concepts Through Real Code
In this section, the focus is on practical implementations, providing code examples for various
interpretability and explainability techniques in AI. These examples will help bridge the gap
between theory and hands-on application, allowing for a deeper understanding of how
interpretability is achieved in practice. They serve as a starting point for exploring these
methods in greater depth. For more complex models or specific use cases, further
customization and deeper understanding will be required.
Example 1: SHAP in a Machine Learning Model
SHAP (SHapley Additive exPlanations) offers insights into the contribution of each feature in a
prediction. Here’s a basic Python example using SHAP with a tree-based model:
import shap
import xgboost
from sklearn.model_selection import train_test_split
import pandas as pd
# Load a sample dataset
data = pd.read_csv('sample_data.csv')
X = data.drop('target', axis=1)
y = data['target']
# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Train an XGBoost model
model = xgboost.XGBClassifier().fit(X_train, y_train)
# Initialize SHAP explainer and calculate SHAP values
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
# Plot SHAP values (for the first prediction in the test set)
7. shap.force_plot(explainer.expected_value, shap_values[0,:], X_test.iloc[0,:])
Example 2: Grad-CAM with a CNN in PyTorch
Grad-CAM is a technique used to visualize the areas in an input image that are important for a
CNN’s decision. Here’s a simple example using PyTorch:
import torch
from torchvision import models, transforms
from PIL import Image
import matplotlib.pyplot as plt
# Function to apply Grad-CAM
def apply_gradcam(model, image_path):
# Preprocess the image
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
img = Image.open(image_path)
input_tensor = preprocess(img).unsqueeze(0)
# Forward pass
output = model(input_tensor)
output_idx = output.argmax()
output_max = output[0, output_idx]
# Backward pass
model.zero_grad()
output_max.backward()
gradients = model.get_activations_gradient()
pooled_gradients = torch.mean(gradients, dim=[0, 2, 3])
# Get the activations and weight them
activations = model.get_activations(input_tensor).detach()
for i in range(activations.shape[1]):
activations[:, i, :, :] *= pooled_gradients[i]
# Generate heatmap
heatmap = torch.mean(activations, dim=1).squeeze()
heatmap = np.maximum(heatmap, 0)
heatmap /= torch.max(heatmap)
plt.matshow(heatmap.squeeze())
8. # Load a pre-trained model
model = models.vgg16(pretrained=True)
# Register hooks to access the gradients and activations
model.register_backward_hooks()
model.register_forward_hooks()
# Apply Grad-CAM
apply_gradcam(model, 'path_to_image.jpg')
Example 3: LIME (Local Interpretable Model-agnostic Explanations)
LIME explains predictions of machine learning models by locally approximating them with
interpretable models.
import lime
import lime.lime_tabular
import sklearn.ensemble
import numpy as np
# Prepare the dataset and model
iris = sklearn.datasets.load_iris()
train, test, labels_train, labels_test = sklearn.model_selection.train_test_split(iris.data, iris.target,
train_size=0.80)
rf = sklearn.ensemble.RandomForestClassifier(n_estimators=500)
rf.fit(train, labels_train)
# Initialize LIME explainer
explainer = lime.lime_tabular.LimeTabularExplainer(train, feature_names=iris.feature_names,
class_names=iris.target_names, discretize_continuous=True)
# Choose a sample to explain
idx = 1
exp = explainer.explain_instance(test[idx], rf.predict_proba, num_features=2)
# Display the explanation
exp.show_in_notebook(show_table=True, show_all=False)
Example 4: Decision Trees as Surrogate Models
Using decision trees to approximate complex models provides an interpretable view of their
decision process.
from sklearn.tree import DecisionTreeClassifier, export_text
from sklearn.model_selection import train_test_split
9. from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
# Load data and create a complex model
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)
complex_model = RandomForestClassifier(n_estimators=100, random_state=0).fit(X_train,
y_train)
# Train a decision tree as a surrogate model
surrogate = DecisionTreeClassifier(max_depth=3)
surrogate.fit(X_train, complex_model.predict(X_train))
# Display the rules
tree_rules = export_text(surrogate, feature_names=iris['feature_names'])
print(tree_rules)
Example 5: Sensitivity Analysis
Sensitivity analysis involves varying input features to see how they affect the output, giving
insights into the model’s dependence on certain features.
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_boston
# Load data
boston = load_boston()
X = boston.data
y = boston.target
feature_names = boston.feature_names
# Train a model
model = RandomForestRegressor()
model.fit(X, y)
# Choose a feature for sensitivity analysis
feature_idx = 5 # 'RM' - average number of rooms
x_vals = np.linspace(min(X[:, feature_idx]), max(X[:, feature_idx]), 100)
predictions = []
# Vary the feature and observe the change in predictions
for val in x_vals:
X_temp = np.copy(X)
10. X_temp[:, feature_idx] = val
predictions.append(model.predict(X_temp).mean())
# Plot
plt.figure(figsize=(10, 6))
plt.plot(x_vals, predictions, label=feature_names[feature_idx])
plt.xlabel(feature_names[feature_idx])
plt.ylabel('Predicted Median Value')
plt.title('Sensitivity Analysis of Feature')
plt.legend()
plt.show()
Section 6: Case Studies and Real-World Applications
Understanding Through Practical Examples
This section highlights various case studies and real-world applications that demonstrate the
importance and effectiveness of interpretability and explainability in AI. These examples offer
insights into how these concepts are applied in different industries and scenarios.
Case Studies in Healthcare
1. Diagnosis and Treatment Recommendations: AI models used for diagnosing
diseases and recommending treatments have benefitted greatly from interpretability. For
instance, models that predict cancer from imaging data can provide visual explanations
for their predictions, which are crucial for medical professionals.
2. Personalized Medicine: AI systems that suggest personalized treatment plans based
on patient data are more trustworthy when they can explain their recommendations. This
allows healthcare professionals to understand the rationale behind a treatment plan
tailored to individual patients.
Financial Services Applications
1. Credit Scoring Models: AI models used in credit scoring can explain why a loan was
approved or denied, which is essential for both regulatory compliance and customer
service.
2. Fraud Detection Systems: In banking, explainable AI systems help in identifying and
explaining fraudulent transactions, thereby enhancing the trust in these systems and
aiding in the investigation process.
Autonomous Systems and Robotics
1. Self-Driving Cars: In the field of autonomous vehicles, explainability is crucial for
understanding the decisions made by the vehicle in critical situations, which is vital for
safety and regulatory approval.
11. 2. Industrial Robotics: In manufacturing, robots equipped with AI that can explain their
actions allow for better human-robot collaboration and troubleshooting.
Retail and Customer Service
1. Personalized Recommendations: E-commerce platforms use AI for personalized
product recommendations. Explainable AI helps in understanding why certain products
are recommended, enhancing customer trust and improving the recommendation
algorithms.
2. Customer Support Chatbots: AI-driven chatbots are more effective when they can
explain their advice or actions, leading to improved customer satisfaction and efficiency.
Ethical AI and Governance
1. Bias Detection: Case studies in detecting and mitigating biases in AI systems highlight
the role of explainable AI in ensuring fairness and ethical AI practices.
2. AI Governance: Organizations implementing AI governance frameworks use
explainability to ensure compliance, transparency, and accountability in their AI
initiatives.
Section 7: Challenges and Limitations in AI
Interpretability and Explainability
Navigating the Complexities
This section addresses the challenges and limitations associated with achieving interpretability
and explainability in AI, particularly in deep learning. It discusses the obstacles AI practitioners
face and the potential trade-offs involved in making complex models more transparent and
understandable.
Balance Between Performance and Interpretability
1. Complexity vs. Clarity: One of the biggest challenges is the inherent trade-off between
model complexity (which often correlates with performance) and interpretability. Simpler
models are generally more interpretable, but they may not perform as well as complex
models like deep neural networks.
2. Loss of Accuracy: In some cases, efforts to increase interpretability can lead to a
reduction in accuracy or predictive power, which can be a significant setback, especially
in applications where performance is critical.
Technical and Practical Challenges
1. Computational Costs: Implementing interpretability and explainability methods can be
computationally expensive, especially for large-scale models and datasets.
12. 2. Lack of Standardization: There is no one-size-fits-all approach to interpretability and
explainability, making it challenging to standardize these processes across different
models and applications.
Ethical and Societal Implications
1. Bias and Fairness: While interpretability can help in detecting biases, it does not
automatically ensure fairness. Misinterpretations or oversimplifications of complex
models can lead to misguided conclusions.
2. Privacy Concerns: In some instances, explaining AI decisions might require revealing
sensitive or personal information used in the decision-making process, raising privacy
concerns.
Theoretical Limitations
1. Incomplete Understanding of Deep Learning: The theoretical foundations of deep
neural networks are still not fully understood. This lack of complete understanding poses
a significant barrier to developing comprehensive interpretability methods.
2. Ambiguity in Interpretations: Interpretations are often subjective and can vary
depending on the person analyzing the model. This ambiguity can make it challenging to
derive definitive conclusions.
Section 8: Future Directions and Research Trends in AI
Interpretability and Explainability
Exploring the Horizon
This section discusses the prospective advancements and emerging research trends in the field
of AI interpretability and explainability. It highlights the potential future developments and how
they might shape the landscape of AI.
Advancements in Interpretability Methods
1. Integration with Advanced AI Models: Continued efforts are expected in integrating
interpretability techniques with more advanced AI models, including newer variants of
neural networks.
2. Automated Interpretability: Research into automating the interpretability process is
likely to gain traction, making it easier and more efficient to apply these techniques in
different scenarios.
Explainability in Complex Systems
1. Explainability in Reinforcement Learning: As reinforcement learning systems become
more prevalent, especially in complex environments, there will be an increased focus on
making these systems interpretable and explainable.
13. 2. Contextual and Situational Explainability: Developing methods that provide
explanations tailored to the specific context or situation, making them more relevant and
easier to understand for end-users.
Ethical and Regulatory Developments
1. Standardization of Interpretability: Efforts towards standardizing what constitutes
‘good’ interpretability in AI systems, potentially leading to industry-wide benchmarks or
guidelines.
2. Regulation-Driven Research: With stricter AI regulations anticipated, research is likely
to align more closely with regulatory requirements, focusing on transparency, fairness,
and accountability.
Human-Centric AI
1. Human-in-the-loop Interpretability: Emphasizing the role of humans in interpreting AI,
including research on how to effectively communicate AI decisions to different
stakeholders.
2. User-Centric Design of Explainability: Tailoring explainability tools and interfaces to
suit the needs and understanding of specific user groups, such as domain experts,
laypersons, or regulatory bodies.
Interdisciplinary Approaches
1. Collaborations Across Fields: Anticipated collaborations between AI researchers,
ethicists, psychologists, and domain experts to develop more holistic interpretability
solutions.
2. Leveraging Psychological Insights: Incorporating findings from cognitive psychology
to design interpretability tools that align with human cognitive processes and biases.
Technological Innovation
1. AI for Interpreting AI: Utilizing AI techniques themselves to aid in interpreting and
explaining complex AI models.
2. Visualization Technologies: Advancements in visualization tools and technologies to
provide more intuitive and insightful representations of AI decision processes.
Final Takeaways
● Interdisciplinary Effort: Achieving meaningful interpretability in AI requires an
interdisciplinary approach, combining technical prowess with ethical, legal, and
psychological insights.
● Dynamic Field: The field of AI interpretability and explainability is dynamic, with
continuous advancements and evolving methodologies. Keeping abreast of these
changes is crucial for practitioners and researchers.
14. ● Ethical Imperative: As AI systems become more integrated into critical aspects of
society, the ethical imperative for these systems to be transparent and understandable
becomes increasingly paramount.
● Collaboration and Standardization: Future progress in this field will likely hinge on
collaborative efforts across industries and the development of standardized approaches
and benchmarks for interpretability.
● Empowerment Through Understanding: Ultimately, the goal of AI interpretability and
explainability is to empower users, stakeholders, and society at large with a clear
understanding of how AI systems make decisions, ensuring these systems are used
responsibly and ethically.
References and Further Reading for AI Interpretability
and Explainability
1. “An empirical comparison of deep learning explainability approaches for EEG
using simulated ground truth” by Akshay Sujatha Ravindran and Jose
Contreras-Vidal. Published in Scientific Reports, this paper compares multiple model
explanation methods for EEG, identifying the most suitable methods and understanding
their limitations. DeepLift was found to be consistently accurate and robust. Link to the
article (Published: 18 October 2023)
2. “Breaking the Paradox of Explainable Deep Learning.” This paper proposes a
method that trains deep hypernetworks to generate explainable linear models. The
proposed method retains the accuracy of black-box deep networks while offering
inherent explainability. Link to the article
3. “Using model explanations to guide deep learning models towards consistent
explanations for EHR data”. This study focuses on enhancing explanation consistency
in deep learning models, particularly in the context of Electronic Health Records. A novel
deep learning ensemble architecture is proposed, significantly improving explanation
consistency. Link to the article (Published: 18 November 2022)
4. “Obtaining genetics insights from deep learning via explainable artificial
intelligence” by Novakovsky, G., Dexter, N., Libbrecht, M.W., et al. This paper explores
the use of explainable AI in the context of genetics and deep learning, highlighting the
significance of interpretability in this domain. Link to the article (Published: 03 October
2022)
5. “Explaining machine learning models with interactive natural language
conversations using TalkToModel”. This paper introduces TalkToModel, a dialogue
system that explains ML models through natural language conversations. It
demonstrates the effectiveness of this approach in making model explainability more
accessible and intuitive. Link to the article (Published: 27 July 2023)
Tags: Deep learning, Explainability, Neural network, Visualization