Explainable Artificial Intelligence - Demystifying the Hype

The field of Artificial Intelligence powered by Machine Learning and Deep Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years. Hence, in the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.

A machine learning or deep learning model by itself consists of an algorithm which tries to learn latent patterns and relationships from data without hard-coding fixed rules. Hence, explaining how a model works to the business always poses its own set of challenges. There are some domains in the industry especially in the world of finance like insurance or banking where data scientists often end up having to use more traditional machine learning models (linear or tree-based). The reason being that model interpretability is very important for the business to explain each and every decision being taken by the model.However, this often leads to a sacrifice in performance. This is where complex models like ensembles and neural networks typically give us better and more accurate performance (since true relationships are rarely linear in nature).We, however, end up being unable to have proper interpretations for model decisions.

To address and talk about these gaps, I will take a conceptual yet hands-on approach where we will explore some of these challenges in-depth about explainable artificial intelligence (XAI) and human interpretable machine learning and even showcase with some examples using state-of-the-art model interpretation frameworks in Python!

 
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Outline/Structure of the Tutorial

The focus of this session is to demystify the hype behind the term 'Explainable AI' and talk about tangible concepts which can be leveraged using state-of-the-art tools and techniques to build human-interpretable models. We will be giving a conceptual overview of what Explainable AI or XAI entails followed by major strategies around XAI techniques. Once the audience gets some foundational knowledge around XAI, we will showcase some case-studies using hands-on examples in Python to build machine learning and deep learning models and leverage model interpretation and explanation strategies. Overall the talk will be structured as follows.

Part 1: The Importance of Human Interpretable Machine Learning

  • Understanding Machine Learning Model Interpretation
  • Importance of Machine Learning Model Interpretation
  • Criteria for Model Interpretation Methods
  • Scope of Model Interpretation

Part 2: Model Interpretation Strategies

  • Traditional Techniques for Model Interpretation
  • Challenges and Limitations of Traditional Techniques
  • The Accuracy vs. Interpretability trade-off
  • Model Interpretation Techniques

Part 3: Hands-on Model Interpretation — A comprehensive Guide

  • Hands-on guides on using the latest state-of-the-art model interpretation frameworks
  • Features, concepts and examples of using frameworks like ELI5, Skater and SHAP
  • Explore concepts and see them in action — Feature importances, partial dependence plots, surrogate models, interpretation and explanations with LIME, SHAP values
  • Hands-on Machine Learning Model Interpretation on a supervised learning example

Part 4: Hands-on Advanced Model Interpretation

  • Hands-on Model Interpretation on Unstructured Datasets
  • Advanced Model Interpretation on Deep Learning Models

Learning Outcome

Key Takeaways from this talk\tutorial

- Understand what is Explainable Artificial Intelligence

- Learn the latest and best techniques for building interpretable models and unbox the opacity of complex black-box models

- Learn how to leverage state-of-the-art model interpretation frameworks in Python

- Understand how to interpret models on both structured and unstructured data

Target Audience

Data Scientists, Engineers, Managers, AI Enthusiasts

Prerequisites for Attendees

Participants are expected to know what is AI, Machine Learning and Deep Learning. Some basics around the Data Science lifecycle including data, features, modeling and evaluation.

Examples will be shown in Python so having a basic knowledge of Python helps.

schedule Submitted 3 months ago

Public Feedback

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  • Naresh Jain
    By Naresh Jain  ~  2 weeks ago
    reply Reply

    Dipanjan, thank you for a very well written proposal on a very important topic.

    Can I request you to give a time break-up of the 4 parts highlighted in the Outline section? I'm concerned 45 mins might to short to cover all the topics in detail.

    • Dipanjan Sarkar
      By Dipanjan Sarkar  ~  2 weeks ago
      reply Reply

      Sure Naresh, you are right, the topic is very vast and usually 45 mins might be too short, however following is what I had in mind similar to what I had done in last year's ODSC.

       

      1. Motivation and need of Model Interpretation - Problems with Bias in Models (10 mins)

      2. Model interpretation Scope and Strategies (10 mins)

      3. Hands-on examples on Model Interpretation for Machine Learning (15 mins)

      - Covers different popular frameworks

      - Covers popular XAI techniques

      4. Hands-on examples on Advanced Model Interpretation (10 mins)

      - Examples on unstructured data (text, images)

      - Deep learning model interpretation

       

      Of course I would be keep the right level of abstraction here depending on the time duration so we maintain 45 minutes but if we have more time I can always dive in further detail also since I'm pretty flexible around the delivery.


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    Advanced

    Intro

    What If I told you that instead of the age-old saying that "a picture is worth a thousand words", it could be that "a word is worth a thousand pictures"?

    Language evolved as an abstraction of distilled information observed and collected from the environment for sophisticated and efficient interpersonal communication and is responsible for humanity's ability to collaborate by storing and sharing experiences. Words represent evocative abstractions over information encoded in our memory and are a composition of many primitive information types.

    That is why language processing is a much more challenging domain and witnessed a delayed 'imagenet' moment.

    One of the cornerstone applications of natural language processing is to leverage the language's inherent structural properties to build a knowledge graph of the world.

    Knowledge Graphs

    Knowledge graph is a form of a rich knowledge base which represents information as an interconnected web of entities and their interactions with each other. This naturally manifests as a graph data structure, where nodes represent entities and the relationship between them are the edges.

    Automatically constructing and leveraging it in an intelligent system is an AI-hard problem, and an amalgamation of a wide variety of fields like natural language processing, information extraction and retrieval, graph algorithms, deep learning, etc.

    It represents a paradigm shift for artificial intelligence systems by going beyond deep learning driven pattern recognition and towards more sophisticated forms of intelligence rooted in reasoning to solve much more complicated tasks.

    To elucidate the differences between reasoning and pattern recognition: consider the problem of computer vision: the vision stack processes an image to detect shapes and patterns in order to identify objects - this is pattern recognition, whereas reasoning is much more complex - to associate detected objects with each other in order to meaningfully describe a scene. For this to be accomplished, a system needs to have a rich understanding of the entities within the scene and their relationships with each other.

    To understand a scene where a person is drinking a can of cola, a system needs to understand concepts like people, that they drink certain liquids via their mouths, liquids can be placed into metallic containers which can be held within a palm to be consumed, and the generational phenomenon that is cola, among others. A sophisticated vision system can then use this rich understanding to fetch details about cola in-order to alert the user of his calorie intake, or to update preferences for a customer. A Knowledge Graph's 'awareness' of the world phenomenons can thus be used to augment a vision system to facilitate such higher order semantic reasoning.

    In production systems though, reasoning may be cast into a pattern recognition problem by limiting the scope of the system for feasibility, but this may be insufficient as the complexity of the system scales or we try to solve general intelligence.

    Challenges in building a Knowledge Graph

    There are two primary challenges towards integrating knowledge graphs in systems: acquisition of knowledge and construction of the graph and effectively leveraging it with robust algorithms to solve reasoning tasks. Creation of the knowledge graph can vary widely depending on the breadth and complexity of the domain - from just manual curation to automatically constructing it by leveraging unstructured/semi-structured sources of knowledge, like books and Wikipedia.

    Many natural language processing tasks are precursors towards building knowledge graphs from unstructured text, like syntactic parsing, information extraction, entity linking, named entity recognition, relationship extraction, semantic parsing, semantic role labeling, entity disambiguation, etc. Open information extraction is an active area of research on extracting semantic triplets of object ('John'), predicate ('eats'), subject ('burger') from plain text, which are used to build the knowledge graph automatically.

    A very interesting approach to this problem is the extraction of frame semantics. Frame semantics relates linguistic semantics to encyclopedic knowledge and the basic idea is that the meaning of a word is linked to all essential knowledge that relates to it, for eg. to understand the word "sell", it's necessary to also know about commercial transactions, which involve a seller, buyer, goods, payment, and the relations between these, which can be represented in a knowledge graph.

    This workshop will focus on building such a knowledge graph from unstructured text.

    Learn good research practices like organizing code and modularizing output for productive data wrangling to improve algorithm performance.

    Knowledge Graph at Embibe

    We will showcase how Embibe's proprietary Knowledge Graph manifests and how it's leveraged across a multitude of projects in our Data Science Lab.

  • Liked Ishita Mathur
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    Ishita Mathur - How GO-FOOD built a Query Semantics Engine to help you find the food you want to order

    Ishita Mathur
    Ishita Mathur
    Data Scientist
    GO-JEK Tech
    schedule 2 weeks ago
    Sold Out!
    45 Mins
    Case Study
    Beginner

    Context: The Search problem

    GOJEK is a SuperApp: 19+ apps within an umbrella app. One of these is GO-FOOD, the first food delivery service in Indonesia and the largest food delivery service in Southeast Asia. There are over 300 thousand restaurants on the platform with a total of over 16 million dishes between them.

    Over two-thirds of those who order food online using GO-FOOD do so by utilising text search. Search engines are so essential to our everyday digital experience that we don’t think twice when using them anymore. Search engines involve two primary tasks: retrieval of documents and ranking them in order of relevance. While improving that ranking is an extremely important part of improving the search experience, actually understanding that query helps give the searcher exactly what they’re looking for. This talk will show you what we are doing to make it easy for users to find what they want.

    GO-FOOD uses the ElasticSearch stack with restaurant and dish indexes to search for what the user types. However, this results in only exact text matches and at most, fuzzy matches. We wanted to create a holistic search experience that not only personalised search results, but also retrieved restaurants and dishes that were more relevant to what the user was looking for. This is being done by not only taking advantage of ElasticSearch features, but also developing a Query semantics engine.

    Query Understanding: What & Why

    This is where Query Understanding comes into the picture: it’s about using NLP to correctly identify the search intent behind the query and return more relevant search results, it’s about the interpretation process even before the results are even retrieved and ranked. The semantic neighbours of the query itself become the focus of the search process: after all, if I don’t understand what you’re trying to ask for, how will I give you what you want?

    In the duration of this talk, you will learn about how we are taking advantage of word embeddings to build a Query Understanding Engine that is holistically designed to make the customer’s experience as smooth as possible. I will go over the techniques we used to build each component of the engine, the data and algorithmic challenges we faced and how we solved each problem we came across.

  • Liked AbdulMajedRaja
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    AbdulMajedRaja / Parul pandey - Become Language Agnostic by Combining the Power of R with Python using Reticulate

    45 Mins
    Tutorial
    Intermediate

    Language Wars have always been there for ages and it's got a new candidate with Data science booming - R vs Python. While the fans are fighting R vs Python, the creators (Hadley Wickham (Chief DS @ RStudio) and Wes McKinney (Creator of Pandas Project)) are working together as Ursa Labs team to create open source data science tools. A similar effort by RStudio has given birth to Reticulate (R Interface to Python) that helps programmers combine R and Python in the same code, session and project and create a new kind of super hero.

  • Liked Ashay Tamhane
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    Ashay Tamhane - Modeling Contextual Changes In User Behaviour In Fashion e-commerce

    Ashay Tamhane
    Ashay Tamhane
    Staff Data Scientist
    Swiggy
    schedule 2 weeks ago
    Sold Out!
    20 Mins
    Talk
    Intermediate

    Impulse purchases are quite frequent in fashion e-commerce; browse patterns indicate fluid context changes across diverse product types probably due to the lack of a well-defined need at the consumer’s end. Data from fashion e-commerce portal indicate that the final product a person ends-up purchasing is often very different from the initial product he/she started the session with. We refer to this characteristic as a ‘context change’. This feature of fashion e-commerce makes understanding and predicting user behaviour quite challenging. Our work attempts to model this characteristic so as to both detect and preempt context changes. Our approach employs a deep Gated Recurrent Unit (GRU) over clickstream data. We show that this model captures context changes better than other non-sequential baseline models.