Explainable Artificial Intelligence - Demystifying the Hype

location_city Bengaluru schedule Aug 8th 01:45 - 02:30 PM place Grand Ball Room 2 people 253 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 1 year ago

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      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 1 year 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 1 year 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

      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 Suvro Shankar Ghosh
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      Suvro Shankar Ghosh - Learning Entity embedding’s form Knowledge Graph

      45 Mins
      Case Study
      Intermediate
      • Over a period of time, a lot of Knowledge bases have evolved. A knowledge base is a structured way of storing information, typically in the following form Subject, Predicate, Object
      • Such Knowledge bases are an important resource for question answering and other tasks. But they often suffer from their incompleteness to resemble all the data in the world, and thereby lack of ability to reason over their discrete Entities and their unknown relationships. Here we can introduce an expressive neural tensor network that is suitable for reasoning over known relationships between two entities.
      • With such a model in place, we can ask questions, the model will try to predict the missing data links within the trained model and answer the questions, related to finding similar entities, reasoning over them and predicting various relationship types between two entities, not connected in the Knowledge Graph.
      • Knowledge Graph infoboxes were added to Google's search engine in May 2012

      What is the knowledge graph?

      ▶Knowledge in graph form!

      ▶Captures entities, attributes, and relationships

      More specifically, the “knowledge graph” is a database that collects millions of pieces of data about keywords people frequently search for on the World wide web and the intent behind those keywords, based on the already available content

      ▶In most cases, KGs is based on Semantic Web standards and have been generated by a mixture of automatic extraction from text or structured data, and manual curation work.

      ▶Structured Search & Exploration
      e.g. Google Knowledge Graph, Amazon Product Graph

      ▶Graph Mining & Network Analysis
      e.g. Facebook Entity Graph

      ▶Big Data Integration
      e.g. IBM Watson

      ▶Diffbot, GraphIQ, Maana, ParseHub, Reactor Labs, SpazioDati