IBM on AI Explainability

Think of how humans interact in a conversation.   We require an appropriate in-context level of explainability to support and trust what we hear.   We would expert an intelligent agent to answer questions like:  Tell me more about that.  Or how did it get to that conclusion?    What data was used?   How will the results of the AI outputs be used?   What are the risks involved using the results? ....

IBM’s Arin Bhowmick explains why AI trust is hard to achieve in the enterprise  By Michael Vizard  @mvizard,   in Venturebeat  February 16, 2021  

While appreciation of the potential impact AI can have on business processes has been building for some time, progress has not nearly been as quick as many initial forecasts led many organizations to expect.

Arin Bhowmick, chief design officer for IBM, explained to VentureBeat what needs to be done to achieve the level of AI explainability that will be required to take AI to the next level in the enterprise. ... ' 

IBM Delivers New AI Capabilities

New developments from IBM, and based on this it seems they are attempting to make AI easier to use and deliver.   Notably things like delivering FAQ is the simplest thing you might want to provide information quickly in a conversation.    And Explain-ability to needed convince.  Plus the ability to translate existing documents into usable intelligence.  All things we did with early AI efforts. so I  see this as good direction for Watson.

IBM announces new AI language, explainability, and automation services

Kyle Wiggers  @Kyle_L_Wiggers   In Venturebeat December 9, 2020 5:00 AM

During IBM’s virtual AI Summit this week, the company announced updates across its Watson family of products in the areas of language, explainability, and workplace automation. A new feature called Reading Comprehension surfaces answers from databases of enterprise documents in response to natural language questions, assigning a confidence score to each response. A novel module in Watson Assistant called FAQ Extraction automatically generates question-and-answer documents. And AI Factsheets automatically captures key facts on a machine learning model’s performance and generates reports to “foster transparency and ensure compliance.”

According to IBM, Reading Comprehension, which was built atop a top-performing question-answering system from IBM Research, is intended to help identify more precise answers in response to queries referring to business documents. Reading Comprehension provides scores that indicate how confident the system is in each answer and is currently in beta in IBM’s AI-powered search service Watson Discovery.    ... "

Complexity of Showing How AI Decides

Thoughtful piece on the problem of how to reasonably provide explanation.  Have now looked at several means of addressing the problem.   Consider carefully the decisions being driven and risks involved.

Grilling the answers: How businesses need to show how AI decides

As artificial intelligence becomes more widespread, so the need to render it explainable increases. How can companies navigate the technical and ethical challenges?
By   Lindsay Clark in Computerweek

Show your working: generations of mathematics students have grown up with this mantra. Getting the right answer is not enough. To get top marks, students must demonstrate how they got there. Now, machines need to do the same.

As artificial intelligence (AI) is used to make decisions affecting employment, finance or justice, as opposed to which film a consumer might want to watch next, the public will insist it explains its working.

Sheffield University professor of AI and robotics Noel Sharkey drove home the point when he told The Guardian that decisions based on machine learning could not be trusted because they were so “infected with biases”.

Sharkey called for an end to the application of machine learning to life-changing decisions until they could be proven safe in the same way that drugs are introduced into healthcare.  ... " 

XAI: Google Explainable AI as a Service

Google takes on Explainable AI, try it free at the link ... Note in contrast IBM's Explainability Toolkit.  Now ask explainability of what, to whom?  Further KDNuggets has a good view of this as 'explainable AI as a service'.

Understand AI output and build trust

Explainable AI is a set of tools and frameworks to help you develop interpretable and inclusive machine learning models and deploy them with confidence. With it, you can understand feature attributions in AutoML Tables and AI Platform and visually investigate model behavior using the What-If Tool. It also further simplifies model governance through continuous evaluation of models managed using AI Platform.

Design interpretable and inclusive AI

Build interpretable and inclusive AI systems from the ground up with tools designed to help detect and resolve bias, drift, and other gaps in data and models. AI Explanations in AutoML Tables and AI Platform provide data scientists with the insight needed to improve data sets or model architecture and debug model performance. The What-If Tool lets you investigate model behavior at a glance.

Simple and fully managed

Deploy AI with confidence

Grow end-user trust and improve transparency with human-interpretable explanations of machine learning models. When deploying a model on AutoML Tables or AI Platform, you get a prediction and a score in real time indicating how much a factor affected the final result. While explanations don’t reveal any fundamental relationships in your data sample or population, they do reflect the patterns the model found in the data. .... "

(Update) AI Explainability Toolkit Talk and Technology

From last weeks talk on the just released open source explainabilty toolkit.   This can be seen as a fundamental part of most conversations.   When we interact with colleagues or with professionals, and get recommendations, we often have to ask the question 'Why?'.  This is an attempt at preloading AI originating answers to that question, based on a number of common templates.

http://cognitive-science.info/wp-content/uploads/2019/09/AIX360-CSIG-V1-2019-09-05.pdf  (Slides)

http://cognitive-science.info/community/weekly-update/  Update: Recording: https://www.youtube.com/watch?v=Yn4yduyoQh4

http://aix360.mybluemix.net/   (Technical link, demos)

What does it take to trust AI decisions ? 
AI is now used in many high-stakes decision making applications.

Addressing:
Is it fair?  Is it easy to understand?  Did anyone tamper with it?  Is it accountable?  

Very good talk, lots of great progress shown here,  but still lots more to do.   Everyone doing serious work with AI systems should examine this work and see how their system could link to this capability.  And extend it.   More to follow.

IBM Research AI Explainability 360 Toolkit

By Vijay Arya, Rachel Bellamy, Pin-Yu Chen,Payel Das, Amit Dhurandhar, MaryJo Fitzgerald,Michael Hind, Samuel Hoffman,Stephanie Houde, Vera Liao, Ronny Luss,Sameep Mehta, Saska Mojsilovic, Sami Mourad,Pablo Pedemonte, John Richards,Prasanna Sattigeri, Moninder Singh,Karthikeyan Shanmugam, Kush Varshney,Dennis Wei, Yunfeng Zhang, Ramya Raghavendra .... 

AI Explainability 360 Toolkit

From today's talk:

http://cognitive-science.info/wp-content/uploads/2019/09/AIX360-CSIG-V1-2019-09-05.pdf (Slides)

http://cognitive-science.info/community/weekly-update/  Update: Recording: https://www.youtube.com/watch?v=Yn4yduyoQh4

http://aix360.mybluemix.net/   (Technical link)

What does it take to trust AI Decisions ? 

AI IS NOW USED IN MANY HIGH-STAKES DECISION MAKING APPLICATIONS

Addressing:
Is it fair?  Is it easy to understand?  Did anyone tamper with it?  Is it accountable?  

Very good talk, lots of great progress shown here,  but still lots more to do.   Everyone doing serious work with AI systems should examine this work and see how their system could link to this capability.  And extend it.   More to follow.

IBM Research AI Explainability 360 Toolkit

By Vijay Arya, Rachel Bellamy, Pin-Yu Chen,Payel Das, Amit Dhurandhar, MaryJo Fitzgerald,Michael Hind, Samuel Hoffman,Stephanie Houde, Vera Liao, Ronny Luss,Sameep Mehta, Saska Mojsilovic, Sami Mourad,Pablo Pedemonte, John Richards,Prasanna Sattigeri, Moninder Singh,Karthikeyan Shanmugam, Kush Varshney,Dennis Wei, Yunfeng Zhang, Ramya Raghavendra

IBM AI Open Source Tool Explainability Talk

Upcoming talk, looks to be quite interesting regarding AI explain-ability open source method.  The talk will be recorded and I will post its location afterwards.

 CSIG (Cognitive Systems Institute Group) Talk - Thursday Sep 5, 2019 - 10:30-11am US Eastern
Title: Al Explainability 360 Toolkit

Speakers: Vijay Arya & Amit Dhurandhar, IBM Research

As AI and ML algorithms make inroads into society, calls are increasing for algorithms to explain their outputs. Affected citizens, government regulators. domain experts. or system developers. present different requirements for explanations. To address these needs we introduce:

AI Explainability 360 (http://aix360.mybluemix.net/)  (good tutorials there) , an open-source software toolkit featuring 8 state-of-the-art explainability methods and 2 evaluation metrics. We provide a taxonomy to help entities require explanations to navigate the space of explanation methods, in the toolkit and in the broader literature.

We have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We discuss enhancements to bring research innovations closer to consumers of explanations. ranging from algorithms. to tutorials and an interactive web demo to introduce AI explainability to different and application domains. Together, the toolkit and taxonomy can help identify gaps where more are needed and provide a platform to incorporate them as they are developed. 

Zoom meeting Link: https://zoom.us/j/7371462221

Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference
Thu, Aug 2, 10:30am US Eastern https://zoom.us/j/7371462221
More Details and recording Here : http://cognitive-science.info/community/weekly-update/