Building trust through Explainable AI
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As AI becomes more and more prevalent in our lives, the decisions it makes for us are becoming more and more impactful on our lives and those of others. How can we help people to have trust in the models we're building? The field of Explainable AI focuses on making any machine learning model interpretable by non experts.
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Explainable AI: Building trustworthy AI models?
Building trustworthy, transparent and unbiased machine learning models?
Get started with explainX that brings state-of-the-art explainability techniques under one roof accessible via one-line of code.
Learn the major modules within the explainX explainable AI and model interpretability framework.
These slides are taken from Raheel's presentation at the UnpackAI's forum on Data Ethics in AI.
Explainable AI
Slide for Arithmer Seminar given by Dr. Daisuke Sato (Arithmer) at Arithmer inc.
The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
“Responsible AI: Tools and Frameworks for Developing AI Solutions,” a Present...
“Responsible AI: Tools and Frameworks for Developing AI Solutions,” a Present...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/09/responsible-ai-tools-and-frameworks-for-developing-ai-solutions-a-presentation-from-intel/
Mrinal Karvir, Senior Cloud Software Engineering Manager at Intel, presents the “Responsible AI: Tools and Frameworks for Developing AI Solutions” tutorial at the May 2023 Embedded Vision Summit.
Over 90% of businesses using AI say trustworthy and explainable AI is critical to business, according to Morning Consult’s IBM Global AI Adoption Index 2021. If not designed with responsible considerations of fairness, transparency, preserving privacy, safety and security, AI systems can cause significant harm to people and society and result in financial and reputational damage for companies.
How can we take a human-centric approach to design AI solutions? How can we identify different types of bias and what tools can we use to mitigate those? What are model cards, and how can we use them to improve transparency? What tools can we use to preserve privacy and improve security? In this talk, Karvir discusses practical approaches to adoption of responsible AI principles. She highlights relevant tools and frameworks and explores industry case studies. She also discusses building a well-defined response plan to help address an AI incident efficiently.Explainable AI (XAI)
Explainable Artificial Intelligence (XAI)
Presented at Lightning Talk session at ICACCI'18 on 20th September 208
An Explainable AI (XAI) or Transparent AI is an artificial intelligence (AI) whose actions can be easily understood by humans. It contrasts with the concept of the "black box" in machine learning, meaning the "interpretability" of the workings of complex algorithms, where even their designers cannot explain why the AI arrived at a specific decision.
https://en.wikipedia.org/wiki/Explainable_Artificial_Intelligence
Explainable AI
Introductory presentation to Explainable AI, defending its main motivations and importance. We describe briefly the main techniques available in March 2020 and share many references to allow the reader to continue his/her studies.
Explainable AI (XAI) - A Perspective
Explainable AI (XAI) is becoming Must-Have NFR for most AI enabled product or solution deployments. Keen to know viewpoints and collaboration opportunities.
Explainable AI
Spark 2019: Equifax's SVP Data & Analytics, Peter Maynard, discusses the notion (and importance) of explainable AI in the financial services sector. He looks at the work Equifax have done to crack open the black box by creating patented AI technology that helps companies make smarter, explainable decisions using AI.
Explainable AI in Industry (WWW 2020 Tutorial)
[Video recording available at https://www.youtube.com/playlist?list=PLewjn-vrZ7d3x0M4Uu_57oaJPRXkiS221]
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, hiring, sales, and lending. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
Recommended
Explainable AI: Building trustworthy AI models?
Building trustworthy, transparent and unbiased machine learning models?
Get started with explainX that brings state-of-the-art explainability techniques under one roof accessible via one-line of code.
Learn the major modules within the explainX explainable AI and model interpretability framework.
These slides are taken from Raheel's presentation at the UnpackAI's forum on Data Ethics in AI.
Explainable AI
Slide for Arithmer Seminar given by Dr. Daisuke Sato (Arithmer) at Arithmer inc.
The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
“Responsible AI: Tools and Frameworks for Developing AI Solutions,” a Present...
“Responsible AI: Tools and Frameworks for Developing AI Solutions,” a Present...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/09/responsible-ai-tools-and-frameworks-for-developing-ai-solutions-a-presentation-from-intel/
Mrinal Karvir, Senior Cloud Software Engineering Manager at Intel, presents the “Responsible AI: Tools and Frameworks for Developing AI Solutions” tutorial at the May 2023 Embedded Vision Summit.
Over 90% of businesses using AI say trustworthy and explainable AI is critical to business, according to Morning Consult’s IBM Global AI Adoption Index 2021. If not designed with responsible considerations of fairness, transparency, preserving privacy, safety and security, AI systems can cause significant harm to people and society and result in financial and reputational damage for companies.
How can we take a human-centric approach to design AI solutions? How can we identify different types of bias and what tools can we use to mitigate those? What are model cards, and how can we use them to improve transparency? What tools can we use to preserve privacy and improve security? In this talk, Karvir discusses practical approaches to adoption of responsible AI principles. She highlights relevant tools and frameworks and explores industry case studies. She also discusses building a well-defined response plan to help address an AI incident efficiently.Explainable AI (XAI)
Explainable Artificial Intelligence (XAI)
Presented at Lightning Talk session at ICACCI'18 on 20th September 208
An Explainable AI (XAI) or Transparent AI is an artificial intelligence (AI) whose actions can be easily understood by humans. It contrasts with the concept of the "black box" in machine learning, meaning the "interpretability" of the workings of complex algorithms, where even their designers cannot explain why the AI arrived at a specific decision.
https://en.wikipedia.org/wiki/Explainable_Artificial_Intelligence
Explainable AI
Introductory presentation to Explainable AI, defending its main motivations and importance. We describe briefly the main techniques available in March 2020 and share many references to allow the reader to continue his/her studies.
Explainable AI (XAI) - A Perspective
Explainable AI (XAI) is becoming Must-Have NFR for most AI enabled product or solution deployments. Keen to know viewpoints and collaboration opportunities.
Explainable AI
Spark 2019: Equifax's SVP Data & Analytics, Peter Maynard, discusses the notion (and importance) of explainable AI in the financial services sector. He looks at the work Equifax have done to crack open the black box by creating patented AI technology that helps companies make smarter, explainable decisions using AI.
Explainable AI in Industry (WWW 2020 Tutorial)
[Video recording available at https://www.youtube.com/playlist?list=PLewjn-vrZ7d3x0M4Uu_57oaJPRXkiS221]
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, hiring, sales, and lending. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
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Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
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Machine learning (ML) is currently disrupting almost every industry and is being used as the core component in many systems. The decisions made by these systems may have a great impact on society and specific individuals and thus the decision-making process has to be clear and explainable so humans can trust it. Explainable AI (XAI) is a rather new field in ML in which researchers try to develop models that are able to explain the decision-making process behind ML models. In this talk, we'll learn about the fundamentals of XAI and discuss why we need to start to integrate XAI with our ML models!
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Watch the video: https://wp.me/p3RLHQ-kQi
Learn more: https://www.anl.gov/topic/science-technology/artificial-intelligence
and
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More and more organizations are embracing AI technology by infusing it in their products and services to to differentiate themselves against their competitors. AI is being utilized in some sensitive areas of human life. In this session let's look at some of principles governing adoption of AI in a responsible manner. Why companies are accelerating adoption of AI?
Increasingly organization are accelerating adoption of AI to differentiate their product and services in the market. Outcomes of this digital transformation that we have seen in the areas of optimizing operations, engaging customers, empowering employees and transforming their products and services.
*List some of the sensitive use cases where AI is being applied
*Why governing AI is important and what are those principles?
*How Microsoft is approaching it?
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Artificial Intelligence is increasingly being used in decisions and processes that are critical for individuals, businesses, and society, especially in areas such as hiring, lending, criminal justice, healthcare, and education. Recent ethical challenges and undesirable outcomes associated with AI systems have highlighted the need for regulations, best practices, and practical tools to help data scientists and ML developers build AI systems that are secure, privacy-preserving, transparent, explainable, fair, and accountable – to avoid unintended and potentially harmful consequences and compliance challenges.
In this tutorial, we will present an overview of responsible AI, highlighting model explainability, fairness, and privacy in AI, key regulations/laws, and techniques/tools for providing understanding around AI/ML systems. Then, we will focus on the application of explainability, fairness assessment/unfairness mitigation, and privacy techniques in industry, wherein we present practical challenges/guidelines for using such techniques effectively and lessons learned from deploying models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning many industries and application domains. Finally, based on our experiences in industry, we will identify open problems and research directions for the AI community.
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Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
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"Artificial Intelligence (AI) is making strides in transforming how we live. From the tech industry embracing AI as the most important technology for the 21st century to governments around the world growing efforts in AI, initiatives are rapidly emerging in the space. In sync with these emerging initiatives including U.S. Department of Energy efforts, Argonne has launched an “AI for Science” initiative aimed at accelerating the development and adoption of AI approaches in scientific and engineering domains with the goal to accelerate research and development breakthroughs in energy, basic science, medicine, and national security, especially where we have significant volumes of data and relatively less developed theory. AI methods allow us to discover patterns in data that can lead to experimental hypotheses and thus link data driven methods to new experiments and new understanding."
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and
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Increasingly organization are accelerating adoption of AI to differentiate their product and services in the market. Outcomes of this digital transformation that we have seen in the areas of optimizing operations, engaging customers, empowering employees and transforming their products and services.
*List some of the sensitive use cases where AI is being applied
*Why governing AI is important and what are those principles?
*How Microsoft is approaching it?
Speaker
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Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
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What are the types of AI / ML? What are the leading business use cases? Why are companies so excited about it?
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How do we protect privacy of users when building large-scale AI based systems? How do we develop machine learned models and systems taking fairness, accountability, and transparency into account? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical, legal, and technical challenges encountered by researchers and practitioners alike. In this talk, we will first motivate the need for adopting a "fairness and privacy by design" approach when developing AI/ML models and systems for different consumer and enterprise applications. We will then focus on the application of fairness-aware machine learning and privacy-preserving data mining techniques in practice, by presenting case studies spanning different LinkedIn applications (such as fairness-aware talent search ranking, privacy-preserving analytics, and LinkedIn Salary privacy & security design), and conclude with the key takeaways and open challenges.
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DataTalkClub Conference, Feb 12 2021
Creating a machine learning model is not an easy task.
Creating a useful machine learning model that gets into production and generates actual business value - is an even harder one.
There are many ways for an ML project or product to fail even when the data is there and the model technically performs well. From the wrong problem statement to lack of trust from stakeholders, in this talk I will discuss what issues to look out for, and how to avoid them.
What is Content Analytics
Content analytics is using rules, mathematical, statistical, and semantic models to automate how content is captured, analyzed, and governed over its lifecycle
Search Marketing: Still the Killer App for Online Marketing
Presentation Ben Lloyd gave for the Portland Business Journal's Biz Dev Seminar series. Covers the ROI of search marketing, SEO fundamentals, and how to integrate SEO & Social Media Marketing.
Gaining Insight into Twitter Audiences through Personality Analysis
This project uses DSX, Apache Spark, the Twitter API and the Personality Insights Watson API to:
1- Understand the personality profile of the average twitter follower of an account
2- Segment those followers through kmeans clustering analysis into the most relevant segments based on the "Needs" features of the profile. This insight will be useful to marketing teams to optimize targeting and messaging to better meet those needs.
Twitter Accounts Analyzed:
@APACHESPARK
Code: https://github.com/anamecheverri/WatsonAPIPersonalityInsights
Best of SEJ Summit: How to Identify Lost & Missed Content Opportunities
Join Rhea Drysdale, CEO of Outspoken Media, as she covers how to identify lost and missed content opportunities that will attract engaged users and build your site’s expertise, authority, and trust.
Business Challenges Solved by APIs: What Every Executive Should Know
It seems like everyone either has or needs an API these days. Analysts are touting how critical it is for businesses to engage their customers across every touchpoint. APIs are supposed to fuel innovation for these new applications, new channels, and, ultimately, new ways to generate revenue.
But getting your API strategy off the ground could be more difficult than it sounds. Even with the right strategy and architecture, there are very real technical challenges in your way.
Join API experts from Elastic Path Software as well as API Evangelist Kin Lane for a frank discussion on the current state of APIs.
How to Successfully Exploit the Power of the Matrix - ENT225 - re:Invent 2017
Leading Edge Forums (LEF) has labelled the synergistic combination of cloud computing and machine intelligence (MI) as ‘the Matrix’: the combination of cloud services such as IaaS, IoT, MI, and edge computing. For companies to thrive, they need to know the answers to the following questions: How are successful companies harnessing the power of the Matrix? How do they structure their organizations? What makes them so agile? How do they attract and retain skilled employees? LEF studies successful businesses and learns what makes them great. Our 6-month research program has dived deep with multiple AWS customers to understand not only their use of the technology, but also the business transformation program that allowed them to maximize the value that AWS provides. Attend this session to learn more about the research that has been done, client examples, observations that the LEF has made and how this can be used to help drive your transformation program.
Session sponsored by DXC Technology
Women in Automation: Exploring RPA - Part 1 of 3
We will start off this session by introducing you to several women who have a career in RPA.
Find out How Robots can help you and your career. In this session, our speaker: Cynthia Libby, UiPath Consulting Director, will share with you some insights on the following topics:
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-Who is UiPath?
-RPA Job Market Overview
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Speaker: Cynthia Libby, UiPath Consulting Director,
User Experience: Building with Hypermedia for Other Folks
Why do certain technologies succeed in upending the development landscape while so many others fall to the wayside? Can we drive commercial success from technological choices we make on a daily basis?
SPA frameworks like React, and Angular, and opinionated libraries like Ruby on Rails, Spring and Django all share a common obsession with the user experience. Many of the worlds largest organizations grew relying on obsessive customer focus. What can we learn from this to enhance our API Design and Strategy?
In this talk we’ll explore how we can utilize hypermedia to improve experiences for our consumers and developers.
3 Ways to Drive Talent Goals with AI
Explore 3 ways that AI can impact talent goals. Discover the full webinar series "Accelerating Talent Transformation with AI" on the Beamery resources section here: https://beamery.com/resources
Artificial Intelligence Impact - What AI is (and isn't) Helping Startups Scal...
(This presentation was created as a short talk for a French Tech Hub event in San Francisco)
Contents:
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- Examples of AI in industry (exploring business models and use cases)
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Pubcon 2023 - In-House SEO Product Management
SEOs fight battles on many fronts, whether it’s proving value to a content director for including keyword research into the content planning process or overcoming objections that arise from dealing with bad SEO agencies. One of the biggest battles is getting prioritization with the development team. It seems that updating buttons and color schemes get immediate priority while SEO requests, like minimizing scripts in the or improving Core Web Vitals, take a backseat and are often held to a higher standard when it comes to proving value.
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Four Techniques to Run AI on Your Business Data
This webinar explores key topics for preparing AI, including
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Practical AI for Business Profit
Preparing Data for AI
From BI to AI
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Government Acquisitions Accelerated AI
https://www.intellectualpoint.com -
Intellectual Point offers the most up-to-date courses to prepare students with marketable skills for today's job market.
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https://arxiv.org/abs/2306.08302
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Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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What will you get from this session?
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2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
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What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Building trust through Explainable AI
- 1. Building Trust Through Explainable AI @peetdenny
- 3. Explainable AI in FS -> Professions in general The LIME Algorithm Why is building trust important in any serious AI application
- 9. LIME "Why Should I Trust You?”: Explaining the Predictions of Any Classifier
- 15. Trust End Consumer Para-professional Regulator Ethics Body
- 16. • Still a new area of research • Slow
- 18. • “If I read a paper that just focuses on the great accuracy that their model achieved, I wouldn’t just go ahead and deploy that into a hospital” • Sameer Singh (co-author of Lime) Data Scientist
- 20. Further Reading • Why should I trust you (original LIME paper), Ribeiro, Singh, Guestrin https://goo.gl/jAn8Qp • Darpa XAI presentation https://goo.gl/zcrXsf • https://www.data-imaginist.com/2017/announcing-lime/