“Responsible AI and AI governance also become a priority for AI on an industrial scale” according to Gartner 2020 hypecycle. 

Machine learning(ML) and Artificial intelligence(AI) solutions are getting deeper in our day to day life. Currently, AI is empowering our convenience at the cost of our privacy. In the last few years, we have heard the news about big techs and startups facing lawsuits because of not compiling with new data governance laws. AI implementation in business has been facing several issues and challenges to solve them.

The aim of this workshop to empower ML/AI researches and industry leaders in understanding and mitigating the risk of AI. The workshop also provides a platform to exchange ideas and discuss some of the open problems in privacy, ethics and fairness surrounding AI.

FORMAT: Pre-recorded videos (More than 7 hours of content) & Colab notebooks

CONTENT: 

  1. Current challenges in business for ML/AI: 66mins
  2. Introduction to Privacy-Preserving ML/AI: 128mins
  3. Understanding the Ethics of AI: 23mins
  4. How to create fairness in AI: 150mins

+ 11 Colab Notebooks

 

Note: the presentation used in the workshop will not be shared with participants.

Content

Current challenges in business for ML/AI 

  • The bias problem
  • Limited knowledge and data 
  • Data security and privacy
  • Trust deficit system 
  • Unexplanatory of AI 
  • Ownership, collection and utilization of datasets
  • Implementation Strategies
  • Legal Issues 
  • Privacy policies 
  • Managing consent of data

Introduction to Privacy-Preserving ML/AI 

  • Distributed privacy-preserving algorithms
  • Federated learning: theory and applications
  • Split learning: theory and applications
  • Differential privacy: theory and applications
  • Homomorphic encryption: theory and applications
  • Secure enclaves
  • Benchmarks and test cases for privacy-preserving solutions
  • Hands-on privacy-preserving federated learning
  • Hands-on differential privacy 
  • Hands-on homomorphic encryption

Understanding the Ethics of AI 

  • Introduction to ethics of artificial intelligence
  • Introduction to the ethics of data collection and algorithmic processes
  • Why Ethics is import to AI practitioners 
  • Formalizing safety and risk management for AI applications

How to create fairness in AI 

  • Individual fairness
  • Group fairness
  • Counterfactual fairness
  • Algorithmic fairness
  • Fairness in Natural language processing 
  • Fairness in computer vision 
  • Feature Engineering and Bias Detection
  • Toolkits for fairness in AI 
  • Hands-on tutorial on tools for analysing fairness in AI deployment 
    • IBM’s AI Fairness 360 
    • Google’s What-If Tool
    • Microsoft’s Fairlearn
    • Lime
    • Google’s Model Card Toolkit
    • audit-AI

Conclusion

  • List of initiatives around the world 
  • Open Source initiatives 

Required Tools

  • Any editor to run the python programs (preferably Google Colab Notebooks)
  • If working on an editor, TensorFlow, Pytorch and Keras must be installed.
  • High-speed internet connection

Prerequisites

  • Basic to moderate level python
  • Basic of machine learning and artificial intelligence  
  • Basics of encryption including and decryption in software engineering
  • Familiarity with Google Colab and GPU environment 

Download the complete workshop at $19.99

 


After payment, you will receive an email with download link of the whole workshop (Videos & notebook links).

ADaSci Members receive a 30% discount.

The workshop is free for CDS Charterholders.

Your Instructor

Krishna Rastogi is Associate Director at Association of Data Scientist. He has experience in research & development, cutting edge engineering to develop products from idea to deployment. He comes with expertise in building computer vision applications using both hardware and software solutions in several domains.
He specialised in edge AI  domains and deploying deep learning models on small hardware devices without taking the raw data from the devices. He conceptualized, researched, and built 35+  product prototypes in Healthcare and Medtech. He presented some of his projects to Late Shri Dr. A P J Abdul Kalam and Shri Ratan Tata. He worked under Prof. Ramesh Raskar, MIT Media Lab, Boston as a visiting student for a year. He also worked with the MIT team to set up an innovation lab in Mumbai.

2 Comments
  1. darpan 3 months ago

    Hi,
    Was looking to attend “privacy to fairness in AI”
    I have 2 queries.

    1) Are these prerequisite valid for this workshop?

    Basic to moderate level python
    Basic of machine learning and artificial intelligence
    Basics of encryption including and decryption in software engineering
    Familiarity with Google Colab and GPU environment

    2) Is there any discount i can get?

  2. Prarthana Thite 2 months ago

    I am a Member of AdaSci and I want to attend workshop on From Privacy to Fairness in AI. I have read that it is free for members. Kindly let me know how to register for this event.

    Thank you

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