Explainable Artificial Intelligence - Demystifying the Hype

schedule Aug 8th 01:45 - 02:30 PM place Grand Ball Room 2 people 252 Interested

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!

 
 

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 11 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Naresh Jain
    By Naresh Jain  ~  8 months 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  ~  8 months 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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    • Redundancy: multiple modalities process the same information
    • Complimentarity: multiple modalities take separate information and merge it
    • Transfer: a modality produces information that another modality consumes
    • Concurrency: multiple modalities take in separate information that is not merged

    Computer - Human Modalities

    Computers utilize a wide range of technologies to communicate and send information to humans:

    • Vision - computer graphics typically through a screen
    • Audition - various audio outputs

    Project Features

    Adaptive: They MUST learn as information changes, and as goals and requirements evolve. They MUST resolve ambiguity and tolerate unpredictability. They MUST be engineered to feed on dynamic data in real time.

    Interactive: They MUST interact easily with users so that those users can define their needs comfortably. They MUST interact with other processors, devices, services, as well as with people.

    Iterative and Stateful: They MUST aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They MUST remember previous interactions in a process and return information that is suitable for the specific application at that point in time.

    Contextual: They MUST understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulation, user profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).

    Project Demos

    Multi-Modal Interaction: https://www.youtube.com/watch?v=jQ8Gq2HWxiA

    Gesture Detection: https://www.youtube.com/watch?v=rDSuCnC8Ei0

    Speech Recognition: https://www.youtube.com/watch?v=AewM3TsjoBk

    Assignment (Hands-on Challenge for Attendees)

    Real-time multi-modal access control system for authorized access to work environment - All the key concepts and individual steps will be demonstrated and explained in this workshop, and the attendees need to customize the generic code or approach for this assignment or hands-on challenge.

  • Liked Rahee Walambe
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    Rahee Walambe / Vishal Gokhale - Processing Sequential Data using RNNs

    Rahee Walambe
    Rahee Walambe
    Research and Teaching Faculty
    Symbiosis Institute of Technology
    Vishal Gokhale
    Vishal Gokhale
    Sr. Consultant
    Xnsio
    schedule 8 months ago
    Sold Out!
    480 Mins
    Workshop
    Beginner

    Data that forms the basis of many of our daily activities like speech, text, videos has sequential/temporal dependencies. Traditional deep learning models, being inadequate to model this connectivity needed to be made recurrent before they brought technologies such as voice assistants (Alexa, Siri) or video based speech translation (Google Translate) to a practically usable form by reducing the Word Error Rate (WER) significantly. RNNs solve this problem by adding internal memory. The capacities of traditional neural networks are bolstered with this addition and the results outperform the conventional ML techniques wherever the temporal dynamics are more important.
    In this full-day immersive workshop, participants will develop an intuition for sequence models through hands-on learning along with the mathematical premise of RNNs.

  • 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 8 months 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.

  • Liked Akshay Bahadur
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    Akshay Bahadur - Minimizing CPU utilization for deep networks

    Akshay Bahadur
    Akshay Bahadur
    SDE-I
    Symantec Softwares
    schedule 10 months ago
    Sold Out!
    45 Mins
    Demonstration
    Beginner

    The advent of machine learning along with its integration with computer vision has enabled users to efficiently to develop image-based solutions for innumerable use cases. A machine learning model consists of an algorithm which draws some meaningful correlation between the data without being tightly coupled to a specific set of rules. It's crucial to explain the subtle nuances of the network along with the use-case we are trying to solve. With the advent of technology, the quality of the images has increased which in turn has increased the need for resources to process the images for building a model. The main question, however, is to discuss the need to develop lightweight models keeping the performance of the system intact.
    To connect the dots, we will talk about the development of these applications specifically aimed to provide equally accurate results without using much of the resources. This is achieved by using image processing techniques along with optimizing the network architecture.
    These applications will range from recognizing digits, alphabets which the user can 'draw' at runtime; developing state of the art facial recognition system; predicting hand emojis, developing a self-driving system, detecting Malaria and brain tumor, along with Google's project of 'Quick, Draw' of hand doodles.
    In this presentation, we will discuss the development of such applications with minimization of CPU usage.

  • Liked Venkatraman J
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    Venkatraman J - Entity Co-occurence and Entity Reputation scoring from Unstructured data using Semantic Knowledge graph

    Venkatraman J
    Venkatraman J
    Sr. data Software engineer
    Metapack
    schedule 9 months ago
    Sold Out!
    20 Mins
    Talk
    Intermediate

    Knowledge representation has been a research for many years in AI world and its continuing further too. Once knowledge is represented, reasoning from that extracted knowledge is done by various inferencing techniques. Initial knowledge bases were built using rules from domain experts and different inferencing techniques like Fuzzy inference, Bayesian inference were applied to extract reasoning from those knowledge bases. Semantic networks is another form of knowledge representation which can represent structured data like WordNet, DBpedia which solves problems in a specific domain by storing entities and relations among entities using onotologies.

    Knowledge graph is another representation technique deeply researched in academia as well as used by businesses in production to augment search relevancy in information retrieval(Google knowledgegraph), improve recommender systems, semantic search applications and also Question answering problems.In this talk i will illustrate the benefits of semantic knowledge graph, how it differs from Semantic ontologies, different technologies involved in building knowledge graph, how i built one to analyse unstructured (twitter data) to discover hidden relationships from the twitter corpus. I will also show how Knowledge graph is data scientist's tool kit to discover hidden relationships and insights from unstructured data quickly.

    In this talk i will show the technology and architecture used to determine entity reputation and entity co-occurence using Knowledge graph.Scoring an entity for reputation is useful in many Natural language processing tasks and applications such as Recommender systems.

  • Liked Dr. Atul Singh
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    Dr. Atul Singh - Endow the gift of eloquence to your NLP applications using pre-trained word embeddings

    45 Mins
    Talk
    Beginner

    Word embeddings are the plinth stones of Natural Language Processing (NLP) applications, used to transform human language into vectors that can be understood and processed by machine learning algorithms. Pre-trained word embeddings enable transfer of prior knowledge about the human language into a new application thereby enabling rapid creation of a scalable and efficient NLP applications. Since the emergence of word2vec in 2013, the word embeddings field has seen rapid developments by leaps and bounds with each new successive word embedding outperforming the prior one.

    The goal of this talk is to demonstrate the efficacy of using pre-trained word embedding to create scalable and robust NLP applications, and to explain to the audience the underlying theory of word embeddings that makes it possible. The talk will cover prominent word vector embeddings such as BERT and ELMo from the recent literature.